Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-3617-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-3617-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 May 2024
Development and technical paper |
| 06 May 2024
| 06 May 2024
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Shuai Wang
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Mengyuan Zhang
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Yueqi Gao
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Peng Wang
Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
Qingyan Fu
Shanghai Environmental Monitoring Center, Shanghai 200235, China
Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
Institute of Eco-Chongming (IEC), Shanghai 200062, China
Related authors
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
Short summary
Short summary
Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Bin Luo, Yuqiang Zhang, Tao Tang, Hongliang Zhang, Jianlin Hu, Jiangshan Mu, Wenxing Wang, and Likun Xue
Atmos. Chem. Phys., 25, 4767–4783, https://doi.org/10.5194/acp-25-4767-2025, https://doi.org/10.5194/acp-25-4767-2025, 2025
Short summary
Short summary
India is facing a severe air pollution crisis that poses significant health risks, particularly from PM2.5 and O3. Our study reveals rising levels of both pollutants from 1995 to 2014, leading to increased premature mortality. While anthropogenic emissions play a significant role, biomass burning also impacts air quality, in particular seasons and regions in India. This study underscores the urgent need for localized policies to protect public health amid escalating environmental challenges.
Zheng Li, Gehui Wang, Binyu Xiao, Rongjie Li, Can Wu, Shaojun Lv, Feng Wu, Qingyan Fu, and Yusen Duan
EGUsphere, https://doi.org/10.5194/egusphere-2025-654, https://doi.org/10.5194/egusphere-2025-654, 2025
Short summary
Short summary
Gas-to-aerosol partitioning of organics were investigated in Shanghai during 2023 dust storm period. We found the partitioning coefficients (Fp) of WSOCs in DS were comparable to those during a haze episode (HE), and aerosol liquid water content primarily drove Fp variation in HE, while pH was the dominant factor in DS. Moreover, an enhanced light absorption of Asian dust by brown carbon, mainly in coarse mode, formation was revealed.
Mingjie Kang, Hongliang Zhang, and Qi Ying
EGUsphere, https://doi.org/10.5194/egusphere-2025-255, https://doi.org/10.5194/egusphere-2025-255, 2025
Short summary
Short summary
This study examines the impacts of reducing nitrogen oxides and volatile organic compounds on ozone (O3), secondary inorganic aerosols (SIA), and OH and NO3 radicals. The results show similar predictions for 8-h O3 but significant variability for SIA and radicals, with differences up to 30 % for SIA and 200 % for radicals across chemical mechanisms and inventories. The findings highlight that evaluating control strategies for SIA and atmospheric oxidation capacity requires an ensemble approach.
Qianqian Gao, Guochao Chen, Xiaohui Lu, Jianmin Chen, Hongliang Zhang, and Xiaofei Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-596, https://doi.org/10.5194/egusphere-2025-596, 2025
Short summary
Short summary
Numerous lakes are shrinking due to climate change and human activities, releasing pollutants from dried lakebeds as dust aerosols. The health risks remain unclear. Recently, Poyang and Dongting Lakes faced record droughts, exposing 99 % and 88 % of their areas. We show lakebed dust can raise PM10 to 637.5 μg/m³ and exceed non-carcinogenic (HQ=4.13) and Cr carcinogenic (~2.10×10⁻⁶) risk thresholds, posing growing health threats.
Xiaofei Qin, Hao Li, Jia Chen, Junjie Wei, Hao Ding, Xiaohao Wang, Guochen Wang, Chengfeng Liu, Da Lu, Shengqian Zhou, Haowen Li, Yucheng Zhu, Ziwei Liu, Qingyan Fu, Juntao Huo, Yanfen Lin, Congrui Deng, Yisheng Zhang, and Kan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-623, https://doi.org/10.5194/egusphere-2025-623, 2025
Short summary
Short summary
Mercury is a persistent toxic pollutant that has equally important anthropogenic and natural sources. This study developed a quantitative method on separating the anthropogenic and natural contributions of total gaseous mercury. The underlying impacts on the sea-air exchange fluxes of mercury are evaluated. The new method developed in this study can be reproducible in other regions and the findings are innovative in the field of mercury sources and biogeochemical cycles.
Jianing Dai, Guy P. Brasseur, Mihalis Vrekoussis, Maria Kanakidou, Kun Qu, Yijuan Zhang, Hongliang Zhang, and Tao Wang
Atmos. Chem. Phys., 24, 12943–12962, https://doi.org/10.5194/acp-24-12943-2024, https://doi.org/10.5194/acp-24-12943-2024, 2024
Short summary
Short summary
This paper employs a regional chemical transport model to quantify the sensitivity of air pollutants and photochemical parameters to specified emission reductions in China for representative winter and summer conditions. The study provides insights into further air quality control in China with reduced primary emissions.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
Short summary
Short summary
Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Zijun Zhang, Weiqi Xu, Yi Zhang, Wei Zhou, Xiangyu Xu, Aodong Du, Yinzhou Zhang, Hongqin Qiao, Ye Kuang, Xiaole Pan, Zifa Wang, Xueling Cheng, Lanzhong Liu, Qingyan Fu, Douglas R. Worsnop, Jie Li, and Yele Sun
Atmos. Chem. Phys., 24, 8473–8488, https://doi.org/10.5194/acp-24-8473-2024, https://doi.org/10.5194/acp-24-8473-2024, 2024
Short summary
Short summary
We investigated aerosol composition and sources and the interaction between secondary organic aerosol (SOA) and clouds at a regional mountain site in southeastern China. Clouds efficiently scavenge more oxidized SOA; however, cloud evaporation leads to the production of less oxidized SOA. The unexpectedly high presence of nitrate in aerosol particles indicates that nitrate formed in polluted areas has undergone interactions with clouds, significantly influencing the regional background site.
Song Gao, Yong Yang, Xiao Tong, Linyuan Zhang, Yusen Duan, Guigang Tang, Qiang Wang, Changqing Lin, Qingyan Fu, Lipeng Liu, and Lingning Meng
Atmos. Meas. Tech., 16, 5709–5723, https://doi.org/10.5194/amt-16-5709-2023, https://doi.org/10.5194/amt-16-5709-2023, 2023
Short summary
Short summary
We optimized and conducted an experimental program for the real-time monitoring of non-methane hydrocarbon instruments using the direct method. Changing the enrichment and specially designed columns further improved the test effect. The results correct the measurement errors that have prevailed for many years and can lay a foundation for the evaluation of volatile organic compounds in the regional ambient air and provide direction for the measurement of low-concentration ambient air pollutants.
Jianing Dai, Guy P. Brasseur, Mihalis Vrekoussis, Maria Kanakidou, Kun Qu, Yijuan Zhang, Hongliang Zhang, and Tao Wang
Atmos. Chem. Phys., 23, 14127–14158, https://doi.org/10.5194/acp-23-14127-2023, https://doi.org/10.5194/acp-23-14127-2023, 2023
Short summary
Short summary
In this study, we used a regional chemical transport model to characterize the different parameters of atmospheric oxidative capacity in recent chemical environments in China. These parameters include the production and destruction rates of ozone and other oxidants, the ozone production efficiency, the OH reactivity, and the length of the reaction chain responsible for the formation of ozone and ROx. They are also affected by the aerosol burden in the atmosphere.
Da Lu, Hao Li, Mengke Tian, Guochen Wang, Xiaofei Qin, Na Zhao, Juntao Huo, Fan Yang, Yanfen Lin, Jia Chen, Qingyan Fu, Yusen Duan, Xinyi Dong, Congrui Deng, Sabur F. Abdullaev, and Kan Huang
Atmos. Chem. Phys., 23, 13853–13868, https://doi.org/10.5194/acp-23-13853-2023, https://doi.org/10.5194/acp-23-13853-2023, 2023
Short summary
Short summary
Environmental conditions during dust are usually not favorable for secondary aerosol formation. However in this study, an unusual dust event was captured in a Chinese mega-city and showed “anomalous” meteorology and a special dust backflow transport pathway. The underlying formation mechanisms of secondary aerosols are probed in the context of this special dust event. This study shows significant implications for the varying dust aerosol chemistry in the future changing climate.
Qianqian Gao, Shengqiang Zhu, Kaili Zhou, Jinghao Zhai, Shaodong Chen, Qihuang Wang, Shurong Wang, Jin Han, Xiaohui Lu, Hong Chen, Liwu Zhang, Lin Wang, Zimeng Wang, Xin Yang, Qi Ying, Hongliang Zhang, Jianmin Chen, and Xiaofei Wang
Atmos. Chem. Phys., 23, 13049–13060, https://doi.org/10.5194/acp-23-13049-2023, https://doi.org/10.5194/acp-23-13049-2023, 2023
Short summary
Short summary
Dust is a major source of atmospheric aerosols. Its chemical composition is often assumed to be similar to the parent soil. However, this assumption has not been rigorously verified. Dust aerosols are mainly generated by wind erosion, which may have some chemical selectivity. Mn, Cd and Pb were found to be highly enriched in fine-dust (PM2.5) aerosols. In addition, estimation of heavy metal emissions from dust generation by air quality models may have errors without using proper dust profiles.
Xiaodong Xie, Jianlin Hu, Momei Qin, Song Guo, Min Hu, Dongsheng Ji, Hongli Wang, Shengrong Lou, Cheng Huang, Chong Liu, Hongliang Zhang, Qi Ying, Hong Liao, and Yuanhang Zhang
Atmos. Chem. Phys., 23, 10563–10578, https://doi.org/10.5194/acp-23-10563-2023, https://doi.org/10.5194/acp-23-10563-2023, 2023
Short summary
Short summary
The atmospheric age of particles reflects how long particles have been formed and suspended in the atmosphere, which is closely associated with the evolution processes of particles. An analysis of the atmospheric age of PM2.5 provides a unique perspective on the evolution processes of different PM2.5 components. The results also shed lights on how to design effective emission control actions under unfavorable meteorological conditions.
Meng Wang, Yusen Duan, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Juntao Huo, Jia Chen, Yanfen Lin, Qingyan Fu, Tao Wang, Junji Cao, and Shun-cheng Lee
Atmos. Chem. Phys., 23, 10313–10324, https://doi.org/10.5194/acp-23-10313-2023, https://doi.org/10.5194/acp-23-10313-2023, 2023
Short summary
Short summary
Hourly elemental carbon (EC) and NOx were continuously measured for 5 years (2016–2020) at a sampling site near a highway in western Shanghai. We use a machine learning model to rebuild the measured EC and NOx, and a business-as-usual (BAU) scenario was assumed in 2020 and compared with the measured EC and NOx.
Jinlong Ma, Shengqiang Zhu, Siyu Wang, Peng Wang, Jianmin Chen, and Hongliang Zhang
Atmos. Chem. Phys., 23, 4311–4325, https://doi.org/10.5194/acp-23-4311-2023, https://doi.org/10.5194/acp-23-4311-2023, 2023
Short summary
Short summary
An updated version of the CMAQ model with biogenic volatile organic compound (BVOC) emissions from MEGAN was applied to study the impacts of different land cover inputs on O3 and secondary organic aerosol (SOA) in China. The estimated BVOC emissions ranged from 25.42 to 37.39 Tg using different leaf area index (LAI) and land cover (LC) inputs. Those differences further induced differences of 4.8–6.9 ppb in O3 concentrations and differences of 5.3–8.4 µg m−3 in SOA concentrations in China.
Peng Wang, Ruhan Zhang, Shida Sun, Meng Gao, Bo Zheng, Dan Zhang, Yanli Zhang, Gregory R. Carmichael, and Hongliang Zhang
Atmos. Chem. Phys., 23, 2983–2996, https://doi.org/10.5194/acp-23-2983-2023, https://doi.org/10.5194/acp-23-2983-2023, 2023
Short summary
Short summary
In China, the number of vehicles has jumped significantly in the last decade. This caused severe traffic congestion and aggravated air pollution. In this study, we developed a new temporal allocation approach to quantify the impacts of traffic congestion. We found that traffic congestion worsens air quality and the health burden across China, especially in the urban clusters. More effective and comprehensive vehicle emission control policies should be implemented to improve air quality in China.
Yizhen Wu, Juntao Huo, Gan Yang, Yuwei Wang, Lihong Wang, Shijian Wu, Lei Yao, Qingyan Fu, and Lin Wang
Atmos. Chem. Phys., 23, 2997–3014, https://doi.org/10.5194/acp-23-2997-2023, https://doi.org/10.5194/acp-23-2997-2023, 2023
Short summary
Short summary
Based on a field campaign in a suburban area of Shanghai during summer 2021, we calculated formaldehyde (HCHO) production rates from 24 volatile organic compounds (VOCs). In addition, HCHO photolysis, reactions with OH radicals, and dry deposition were considered for the estimation of HCHO loss rates. Our results reveal the key precursors of HCHO and suggest that HCHO wet deposition may be an important loss term on cloudy and rainy days, which needs to be further investigated.
Changqin Yin, Jianming Xu, Wei Gao, Liang Pan, Yixuan Gu, Qingyan Fu, and Fan Yang
Atmos. Chem. Phys., 23, 1329–1343, https://doi.org/10.5194/acp-23-1329-2023, https://doi.org/10.5194/acp-23-1329-2023, 2023
Short summary
Short summary
The particle matter (PM2.5) at the top of the 632 m high Shanghai Tower was found to be higher than the surface from June to October due to unexpected larger PM2.5 levels during early to middle afternoon at Shanghai Tower. We suppose the significant chemical production of secondary species existed in the mid-upper planetary boundary layer. We found a high nitrate concentration at the tower site for both daytime and nighttime in winter, implying efficient gas-phase and heterogeneous formation.
Xiaofei Qin, Shengqian Zhou, Hao Li, Guochen Wang, Cheng Chen, Chengfeng Liu, Xiaohao Wang, Juntao Huo, Yanfen Lin, Jia Chen, Qingyan Fu, Yusen Duan, Kan Huang, and Congrui Deng
Atmos. Chem. Phys., 22, 15851–15865, https://doi.org/10.5194/acp-22-15851-2022, https://doi.org/10.5194/acp-22-15851-2022, 2022
Short summary
Short summary
Using artificial neural network modeling and an explainable analysis approach, natural surface emissions (NSEs) were identified as a main driver of gaseous elemental mercury (GEM) variations during the COVID-19 lockdown. A sharp drop in GEM concentrations due to a significant reduction in anthropogenic emissions may disrupt the surface–air exchange balance of Hg, leading to increases in NSEs. This implies that NSEs may pose challenges to the future control of Hg pollution.
Meng Wang, Yusen Duan, Wei Xu, Qiyuan Wang, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Haijie Tong, Juntao Huo, Jia Chen, Shan Gao, Zhongbiao Wu, Long Cui, Yu Huang, Guangli Xiu, Junji Cao, Qingyan Fu, and Shun-cheng Lee
Atmos. Chem. Phys., 22, 12789–12802, https://doi.org/10.5194/acp-22-12789-2022, https://doi.org/10.5194/acp-22-12789-2022, 2022
Short summary
Short summary
In this study, we report the long-term measurement of organic carbon (OC) and elementary carbon (EC) in PM2.5 with hourly time resolution conducted at a regional site in Shanghai from 2016 to 2020. The results from this study provide critical information about the long-term trend of carbonaceous aerosol, in particular secondary OC, in one of the largest megacities in the world and are helpful for developing pollution control measures from a long-term planning perspective.
Han Zang, Yue Zhao, Juntao Huo, Qianbiao Zhao, Qingyan Fu, Yusen Duan, Jingyuan Shao, Cheng Huang, Jingyu An, Likun Xue, Ziyue Li, Chenxi Li, and Huayun Xiao
Atmos. Chem. Phys., 22, 4355–4374, https://doi.org/10.5194/acp-22-4355-2022, https://doi.org/10.5194/acp-22-4355-2022, 2022
Short summary
Short summary
Particulate nitrate plays an important role in wintertime haze pollution in eastern China, yet quantitative constraints on detailed nitrate formation mechanisms remain limited. Here we quantified the contributions of the heterogeneous N2O5 hydrolysis (66 %) and gas-phase OH + NO2 reaction (32 %) to nitrate formation in this region and identified the atmospheric oxidation capacity (i.e., availability of O3 and OH radicals) as the driving factor of nitrate formation from both processes.
Peng Wang, Juanyong Shen, Men Xia, Shida Sun, Yanli Zhang, Hongliang Zhang, and Xinming Wang
Atmos. Chem. Phys., 21, 10347–10356, https://doi.org/10.5194/acp-21-10347-2021, https://doi.org/10.5194/acp-21-10347-2021, 2021
Short summary
Short summary
Ozone (O3) pollution has received extensive attention due to worsening air quality and rising health risks. The Chinese National Day holiday (CNDH), which is associated with intensive commercial and tourist activities, serves as a valuable experiment to evaluate the O3 response during the holiday. We find sharply increasing trends of observed O3 concentrations throughout China during the CNDH, leading to 33 % additional total daily deaths.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
Short summary
Short summary
In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Jinlong Ma, Juanyong Shen, Peng Wang, Shengqiang Zhu, Yu Wang, Pengfei Wang, Gehui Wang, Jianmin Chen, and Hongliang Zhang
Atmos. Chem. Phys., 21, 7343–7355, https://doi.org/10.5194/acp-21-7343-2021, https://doi.org/10.5194/acp-21-7343-2021, 2021
Short summary
Short summary
Due to the reduced anthropogenic emissions during the COVID-19 lockdown, mainly from the transportation and industrial sectors, PM2.5 decreased significantly in the whole Yangtze River Delta (YRD) and its major cities. However, the contributions and relative importance of different source sectors and regions changed differently, indicating that control strategies should be adjusted accordingly for further pollution control.
Kun Zhang, Ling Huang, Qing Li, Juntao Huo, Yusen Duan, Yuhang Wang, Elly Yaluk, Yangjun Wang, Qingyan Fu, and Li Li
Atmos. Chem. Phys., 21, 5905–5917, https://doi.org/10.5194/acp-21-5905-2021, https://doi.org/10.5194/acp-21-5905-2021, 2021
Short summary
Short summary
Recently, high O3 concentrations were frequently observed in rural areas of the Yangtze River Delta (YRD) region under stagnant conditions. Using an online measurement and observation-based model, we investigated the budget of ROx radicals and the influence of isoprene chemistry on O3 formation. Our results underline that isoprene chemistry in the rural atmosphere becomes important with the participation of anthropogenic NOx.
Mengyuan Zhang, Arpit Katiyar, Shengqiang Zhu, Juanyong Shen, Men Xia, Jinlong Ma, Sri Harsha Kota, Peng Wang, and Hongliang Zhang
Atmos. Chem. Phys., 21, 4025–4037, https://doi.org/10.5194/acp-21-4025-2021, https://doi.org/10.5194/acp-21-4025-2021, 2021
Short summary
Short summary
We studied changes in air quality in India induced by the COVID-19 lockdown through both surface observations and the CMAQ model. Our results show that emission reductions improved the air quality across India during the lockdown. On average, the levels of PM2.5 and O3 decreased by 28 % and 15 %, indicating positive effects of lockdown measures. We suggest that more stringent and localized emission control strategies should be implemented in India to mitigate air pollutions.
Junjun Deng, Hao Guo, Hongliang Zhang, Jialei Zhu, Xin Wang, and Pingqing Fu
Atmos. Chem. Phys., 20, 14419–14435, https://doi.org/10.5194/acp-20-14419-2020, https://doi.org/10.5194/acp-20-14419-2020, 2020
Short summary
Short summary
One-year source apportionment of BC aerosols in a coastal city in China was conducted with the light-absorption observation-based method and source-oriented model. Source contributions identified by the two source apportionment methods were compared. Temporal variability, potential sources and transport pathways of BC from fossil fuel and biomass burning were characterized. Significant influence of biomass burning in North and East–Central China on BC in the region was highlighted.
Zhihao Shi, Lin Huang, Jingyi Li, Qi Ying, Hongliang Zhang, and Jianlin Hu
Atmos. Chem. Phys., 20, 13455–13466, https://doi.org/10.5194/acp-20-13455-2020, https://doi.org/10.5194/acp-20-13455-2020, 2020
Short summary
Short summary
Meteorological conditions play important roles in the formation of O3 and PM2.5 pollution in China. O3 is most sensitive to temperature and the sensitivity is dependent on the O3 chemistry formation or loss regime. PM2.5 is negatively sensitive to temperature, wind speed, and planetary boundary layer height and positively sensitive to humidity. The results imply that air quality in certain regions of China is sensitive to climate changes.
Rui Li, Qiongqiong Wang, Xiao He, Shuhui Zhu, Kun Zhang, Yusen Duan, Qingyan Fu, Liping Qiao, Yangjun Wang, Ling Huang, Li Li, and Jian Zhen Yu
Atmos. Chem. Phys., 20, 12047–12061, https://doi.org/10.5194/acp-20-12047-2020, https://doi.org/10.5194/acp-20-12047-2020, 2020
Xiaofei Qin, Leiming Zhang, Guochen Wang, Xiaohao Wang, Qingyan Fu, Jian Xu, Hao Li, Jia Chen, Qianbiao Zhao, Yanfen Lin, Juntao Huo, Fengwen Wang, Kan Huang, and Congrui Deng
Atmos. Chem. Phys., 20, 10985–10996, https://doi.org/10.5194/acp-20-10985-2020, https://doi.org/10.5194/acp-20-10985-2020, 2020
Short summary
Short summary
The uncertainties in mercury emissions are much larger from natural sources than anthropogenic sources. A method was developed to quantify the contributions of natural surface emissions to ambient GEM based on PMF modeling. The annual GEM concentration in eastern China showed a decreasing trend from 2015 to 2018, while the relative contribution of natural surface emissions increased significantly from 41 % in 2015 to 57 % in 2018, gradually surpassing those from anthropogenic sources.
Jian Xu, Jia Chen, Na Zhao, Guochen Wang, Guangyuan Yu, Hao Li, Juntao Huo, Yanfen Lin, Qingyan Fu, Hongyu Guo, Congrui Deng, Shan-Hu Lee, Jianmin Chen, and Kan Huang
Atmos. Chem. Phys., 20, 7259–7269, https://doi.org/10.5194/acp-20-7259-2020, https://doi.org/10.5194/acp-20-7259-2020, 2020
Short summary
Short summary
This study provided evidence that gas-particle partitioning of ammonia, as opposed to ammonia concentration, plays a critical role in the haze formation. A reduction in ammonia emissions alone may not reduce air pollution effectively, at least at rural agricultural sites in China.
Meng Gao, Jinhui Gao, Bin Zhu, Rajesh Kumar, Xiao Lu, Shaojie Song, Yuzhong Zhang, Beixi Jia, Peng Wang, Gufran Beig, Jianlin Hu, Qi Ying, Hongliang Zhang, Peter Sherman, and Michael B. McElroy
Atmos. Chem. Phys., 20, 4399–4414, https://doi.org/10.5194/acp-20-4399-2020, https://doi.org/10.5194/acp-20-4399-2020, 2020
Short summary
Short summary
A regional fully coupled meteorology–chemistry model, Weather Research and Forecasting model with Chemistry (WRF-Chem), was employed to study the seasonality of ozone (O3) pollution and its sources in both China and India.
Jianming Xu, Xuexi Tie, Wei Gao, Yanfen Lin, and Qingyan Fu
Atmos. Chem. Phys., 19, 9017–9035, https://doi.org/10.5194/acp-19-9017-2019, https://doi.org/10.5194/acp-19-9017-2019, 2019
Short summary
Short summary
The PM2.5 in China has decreased significantly in recent years as a result of the implementation of the Chinese Clean Air Action Plan in 2013, while the O3 pollution is getting worse, especially in megacities. The work aims to better understand the elevated O3 pollution in the megacity of Shanghai, China, and its response to emission changes, which is important for developing an effective emission control strategy in the future.
Xinning Wang, Yin Shen, Yanfen Lin, Jun Pan, Yan Zhang, Peter K. K. Louie, Mei Li, and Qingyan Fu
Atmos. Chem. Phys., 19, 6315–6330, https://doi.org/10.5194/acp-19-6315-2019, https://doi.org/10.5194/acp-19-6315-2019, 2019
Short summary
Short summary
Shipping emissions were measured online at Shanghai Port, and their impacts on local air quality at the port and in the surrounding area were quantitatively assessed. Ship emission plumes were readily detectable before they dissipated. We captured ship emission plumes using synchronized peaks of SO2 and vanadium particles. By measuring the pollutant concentrations during plumes and their occurrence frequency, we made quantitative estimations of ship emission impacts on port air quality.
Junlan Feng, Yan Zhang, Shanshan Li, Jingbo Mao, Allison P. Patton, Yuyan Zhou, Weichun Ma, Cong Liu, Haidong Kan, Cheng Huang, Jingyu An, Li Li, Yin Shen, Qingyan Fu, Xinning Wang, Juan Liu, Shuxiao Wang, Dian Ding, Jie Cheng, Wangqi Ge, Hong Zhu, and Katherine Walker
Atmos. Chem. Phys., 19, 6167–6183, https://doi.org/10.5194/acp-19-6167-2019, https://doi.org/10.5194/acp-19-6167-2019, 2019
Short summary
Short summary
This study aims to estimate the emissions, air quality and population exposure impacts of shipping in 2015, prior to the implementation of the DECAs. It shows that ship emissions within 12 NM of the shore could account for over 55 % of the shipping impact on air pollution in the YRD in summer. Ships entering the Yangtze River and other inland waterways of Shanghai contribute 40–80 % of the ship-related air pollution and population exposure，which both have important implications regarding policy.
Xiaofei Qin, Xiaohao Wang, Yijie Shi, Guangyuan Yu, Na Zhao, Yanfen Lin, Qingyan Fu, Dongfang Wang, Zhouqing Xie, Congrui Deng, and Kan Huang
Atmos. Chem. Phys., 19, 5923–5940, https://doi.org/10.5194/acp-19-5923-2019, https://doi.org/10.5194/acp-19-5923-2019, 2019
Short summary
Short summary
The seasonal pattern of atmospheric mercury species over a regional transport intersection zone in east China indicated impacts from both natural re-emissions and anthropogenic emissions. Quasi-local sources were more important than long-range transport for mercury, opposite from particles. Shipping activities were especially outstanding emissions. Abnormally high GOM was ascribed to the high oxidant levels. The gas–particle partition inhibited the formation of GOM under high particle levels.
Xue Qiao, Hao Guo, Ya Tang, Pengfei Wang, Wenye Deng, Xing Zhao, Jianlin Hu, Qi Ying, and Hongliang Zhang
Atmos. Chem. Phys., 19, 5791–5803, https://doi.org/10.5194/acp-19-5791-2019, https://doi.org/10.5194/acp-19-5791-2019, 2019
Short summary
Short summary
A source-oriented version of the CMAQ model was used to quantify contributions from nine regions to PM2.5 and its components in the 18 cities within Sichuan Basin. Nonlocal emissions contribute 39–66 % and 25–52 % to the citywide average PM2.5 concentrations of 45–126 and 14–31 µg m3 in the winter and summer, respectively. This study demonstrates the importance of joint emission control efforts among cities within the SCB and neighboring regions to the east.
Zhaofeng Tan, Keding Lu, Meiqing Jiang, Rong Su, Hongli Wang, Shengrong Lou, Qingyan Fu, Chongzhi Zhai, Qinwen Tan, Dingli Yue, Duohong Chen, Zhanshan Wang, Shaodong Xie, Limin Zeng, and Yuanhang Zhang
Atmos. Chem. Phys., 19, 3493–3513, https://doi.org/10.5194/acp-19-3493-2019, https://doi.org/10.5194/acp-19-3493-2019, 2019
Short summary
Short summary
We evaluated the atmospheric oxidation capacity (AOC) in four Chinese megacities during photochemically polluted seasons. The chemical production of ozone and particle nitrate was diagnosed through a box model, which can be attributed to daytime radical chemistry. Our work highlights that the formation of both ozone and fine particles is largely driven by the atmospheric radical chemistry in China. Consequently, we suggest future pollution mitigation strategies should consider the role of AOC.
Hao Guo, Sri Harsha Kota, Kaiyu Chen, Shovan Kumar Sahu, Jianlin Hu, Qi Ying, Yuan Wang, and Hongliang Zhang
Atmos. Chem. Phys., 18, 15219–15229, https://doi.org/10.5194/acp-18-15219-2018, https://doi.org/10.5194/acp-18-15219-2018, 2018
Short summary
Short summary
A total of 1.04 million premature mortalities and up to 2 years of life lost (YLL) per person were estimated in India in 2015 due to PM2.5. Premature mortality due to cerebrovascular disease (CEVD) was the highest (0.44 million), followed by ischaemic heart disease (IHD, 0.40 million). The residential sector was the largest contributor, followed by industry, agriculture and energy. Reducing PM2.5 concentrations would lead to a significant reduction in premature mortality and YLL.
Mingjie Kang, Pingqing Fu, Kimitaka Kawamura, Fan Yang, Hongliang Zhang, Zhengchen Zang, Hong Ren, Lujie Ren, Ye Zhao, Yele Sun, and Zifa Wang
Atmos. Chem. Phys., 18, 13947–13967, https://doi.org/10.5194/acp-18-13947-2018, https://doi.org/10.5194/acp-18-13947-2018, 2018
Short summary
Short summary
Molecular characterization and spatial distribution of biogenic primary organic aerosol (POA) and secondary organic aerosol (SOA) in the marine atmosphere are not well known. Here, we analysed the organic molecular composition of marine aerosols collected during a marine cruise in the East China Sea during May–June 2014. Our results suggest that the Asian continent can be a natural emitter of biogenic POA and SOA, which can be transported to the downwind marine atmosphere.
Congbo Song, Yan Liu, Shida Sun, Luna Sun, Yanjie Zhang, Chao Ma, Jianfei Peng, Qian Li, Jinsheng Zhang, Qili Dai, Baoshuang Liu, Peng Wang, Yi Zhang, Ting Wang, Lin Wu, Min Hu, and Hongjun Mao
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-387, https://doi.org/10.5194/acp-2018-387, 2018
Revised manuscript not accepted
Short summary
Short summary
Vehicular emission is a key contributor to ambient volatile organic compounds (VOCs) and NOx in Chinese megacities. Information on real-world emission factors (EFs) for a typical urban fleet is still limited. We found that improvement of fuel quality can significantly reduce feet-average EFs of VOCs (especially for BTEX). Our study provided implications for O3 control in China from the view of primary emission, and highlighted the importance of further control of evaporative emissions.
Qiongzhen Wang, Xinyi Dong, Joshua S. Fu, Jian Xu, Congrui Deng, Yilun Jiang, Qingyan Fu, Yanfen Lin, Kan Huang, and Guoshun Zhuang
Atmos. Chem. Phys., 18, 3505–3521, https://doi.org/10.5194/acp-18-3505-2018, https://doi.org/10.5194/acp-18-3505-2018, 2018
Short summary
Short summary
A synergy of ground-based atmospheric chemistry observation, lidar, and numerical modeling was used to investigate a super dust event passing over Shanghai. The degree of dust that was modified by anthropogenic sources highly depended on the transport pathways. A community regional air quality model with improved dust scheme reproduced reasonable dust chemistry results. The chemical and optical properties of evolving dust are crucial for evaluating the climatic effects of dust.
Jianlin Hu, Xun Li, Lin Huang, Qi Ying, Qiang Zhang, Bin Zhao, Shuxiao Wang, and Hongliang Zhang
Atmos. Chem. Phys., 17, 13103–13118, https://doi.org/10.5194/acp-17-13103-2017, https://doi.org/10.5194/acp-17-13103-2017, 2017
Short summary
Short summary
The model performance of CMAQ with WRF using four different emission inventories in China was validated and compared to obtain the best air pollutants prediction for health effect studies of severe air pollution. The differences in performance of chemical transport model were analyzed for different months and regions in the vast part of China and ensemble predictions were firstly obtained from different inventories for health analysis with minimized errors for pollutants including PM2.5 and O3.
Jianlin Hu, Shantanu Jathar, Hongliang Zhang, Qi Ying, Shu-Hua Chen, Christopher D. Cappa, and Michael J. Kleeman
Atmos. Chem. Phys., 17, 5379–5391, https://doi.org/10.5194/acp-17-5379-2017, https://doi.org/10.5194/acp-17-5379-2017, 2017
Short summary
Short summary
Organic aerosol is a major constituent of ultrafine particulate matter (PM0.1). In this study, a source-oriented air quality model was used to simulate the concentrations and sources of primary and secondary organic aerosols in PM0.1 in California for a 9-year modeling period to provide useful information for epidemiological studies to further investigate the associations with health outcomes.
Jianlin Hu, Peng Wang, Qi Ying, Hongliang Zhang, Jianjun Chen, Xinlei Ge, Xinghua Li, Jingkun Jiang, Shuxiao Wang, Jie Zhang, Yu Zhao, and Yingyi Zhang
Atmos. Chem. Phys., 17, 77–92, https://doi.org/10.5194/acp-17-77-2017, https://doi.org/10.5194/acp-17-77-2017, 2017
Short summary
Short summary
An annual simulation of secondary organic aerosol (SOA) concentrations in China with updated SOA formation pathways reveals that SOA can be a significant contributor to PM2.5 in major urban areas. Summer SOA is dominated by emissions from biogenic sources, while winter SOA is dominated by anthropogenic emissions such as alkanes and aromatic compounds. Reactive surface uptake of dicarbonyls throughout the year and isoprene epoxides in summer is the most important contributor.
Jianlin Hu, Jianjun Chen, Qi Ying, and Hongliang Zhang
Atmos. Chem. Phys., 16, 10333–10350, https://doi.org/10.5194/acp-16-10333-2016, https://doi.org/10.5194/acp-16-10333-2016, 2016
Short summary
Short summary
A yearlong (2013) air-quality simulation was conducted to provide detailed temporal and spatial information of ozone, PM2.5 total and chemical components. The paper firstly compared the simulated air pollutants in China with country-wide public available observations for a whole year. It proves the ability of CMAQ in reproducing severe air pollution in China, shows directions that need to be improved, and benefits future source apportionment and human exposure studies.
Hsiang-He Lee, Shu-Hua Chen, Michael J. Kleeman, Hongliang Zhang, Steven P. DeNero, and David K. Joe
Atmos. Chem. Phys., 16, 8353–8374, https://doi.org/10.5194/acp-16-8353-2016, https://doi.org/10.5194/acp-16-8353-2016, 2016
Short summary
Short summary
A source-oriented CCN module was implemented in a source-oriented chemistry model to study the effect of aerosol mixing state on fog formation. The fraction of aerosols activating into CCN at a supersaturation of 0.5 % in the Central Valley decreased from 94 % in the internal mixture model to 80 % in the source-oriented model. The internal mixture model predicted greater CCN activation than the source-oriented model due to artificial coating of hydrophobic particles with hygroscopic components.
J. Hu, H. Zhang, Q. Ying, S.-H. Chen, F. Vandenberghe, and M. J. Kleeman
Atmos. Chem. Phys., 15, 3445–3461, https://doi.org/10.5194/acp-15-3445-2015, https://doi.org/10.5194/acp-15-3445-2015, 2015
Short summary
Short summary
Air quality model simulations have been conducted for California from 2000 to 2009 with 4km spatial resolution to provide exposure data for health effect studies. Comprehensive analysis shows that predicted concentrations for many pollutants are in agreement with measurements at monitoring stations, building confidence that the fields may be useful at times and locations where measurements are not available. Data can be downloaded for free at http://faculty.engineering.ucdavis.edu/kleeman/.
S. Xiao, M. Y. Wang, L. Yao, M. Kulmala, B. Zhou, X. Yang, J. M. Chen, D. F. Wang, Q. Y. Fu, D. R. Worsnop, and L. Wang
Atmos. Chem. Phys., 15, 1769–1781, https://doi.org/10.5194/acp-15-1769-2015, https://doi.org/10.5194/acp-15-1769-2015, 2015
H. Zhang, S. P. DeNero, D. K. Joe, H.-H. Lee, S.-H. Chen, J. Michalakes, and M. J. Kleeman
Atmos. Chem. Phys., 14, 485–503, https://doi.org/10.5194/acp-14-485-2014, https://doi.org/10.5194/acp-14-485-2014, 2014
Related subject area
Atmospheric sciences
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Chempath 1.0: an open-source pathway analysis program for photochemical models
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Atmospheric moisture tracking with WAM2layers v3
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Development of a High-Resolution Coupled SHiELD-MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025, https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Short summary
A numerical model that simulates the measurement processes behind the ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal, as fine ash particles are smaller than raindrops and remain undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies that balance the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025, https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
Short summary
Photochemical models describe how the composition of the atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth's and other planets' atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025, https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Short summary
Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line And Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, this model is valuable for airglow research and astronomical observatories.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025, https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary
Short summary
We introduce a new version of WAM2layers (Water Accounting Model – 2 layers), a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data had become a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent, reliable, and easier to maintain.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Short summary
SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Short summary
It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
Short summary
Short summary
Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Short summary
This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Short summary
This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
Short summary
In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Short summary
This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary
Short summary
This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Short summary
We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Short summary
Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
Short summary
We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
Short summary
Short summary
The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
Short summary
Short summary
The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
Short summary
Short summary
We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Short summary
The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Short summary
The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
Short summary
Short summary
Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
Short summary
This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
Short summary
Short summary
Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
Short summary
Short summary
Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
Short summary
Short summary
This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
Short summary
Short summary
This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
Short summary
Short summary
An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
Short summary
Short summary
As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
Cited articles
Aleksankina, K., Reis, S., Vieno, M., and Heal, M. R.: Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model, Atmos. Chem. Phys., 19, 2881–2898, https://doi.org/10.5194/acp-19-2881-2019, 2019.
Bai, K., Li, K., Ma, M., Li, K., Li, Z., Guo, J., Chang, N.-B., Tan, Z., and Han, D.: LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion, Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, 2022.
Beekmann, M. and Derognat, C.: Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the atmospheric pollution over the Paris area (ESQUIF) campaign, J. Geophys. Res.-Atmos., 108, 8859, 10.1029/2003JD003391, 2003.
Bei, N., Wu, J., Elser, M., Feng, T., Cao, J., El-Haddad, I., Li, X., Huang, R., Li, Z., Long, X., Xing, L., Zhao, S., Tie, X., Prévôt, A. S. H., and Li, G.: Impacts of meteorological uncertainties on the haze formation in Beijing–Tianjin–Hebei (BTH) during wintertime: a case study, Atmos. Chem. Phys., 17, 14579–14591, https://doi.org/10.5194/acp-17-14579-2017, 2017.
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, 2016.
Binkowski, F. S. and Roselle, S. J.: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res.-Atmos., 108, 4183, https://doi.org/10.1029/2001JD001409, 2003.
Carlton, A. G., Turpin, B. J., Altieri, K. E., Seitzinger, S., Reff, A., Lim, H.-J., and Ervens, B.: Atmospheric oxalic acid and SOA production from glyoxal: Results of aqueous photooxidation experiments, Atmos. Environ., 41, 7588–7602, https://doi.org/10.1016/j.atmosenv.2007.05.035, 2007.
Carter, W. P. and Heo, G.: Development of revised SAPRC aromatics mechanisms, Atmos. Environ., 77, 404–414, 2013.
Chen, S., Wang, H., Lu, K., Zeng, L., Hu, M., and Zhang, Y.: The trend of surface ozone in Beijing from 2013 to 2019: Indications of the persisting strong atmospheric oxidation capacity, Atmos. Environ., 242, 117801, https://doi.org/10.1016/j.atmosenv.2020.117801, 2020.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, 13–17 August 2016, WOS:000485529800092, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Chen, Z. Y., Chen, D. L., Zhao, C. F., Kwan, M. P., Cai, J., Zhuang, Y., Zhao, B., Wang, X. Y., Chen, B., Yang, J., Li, R. Y., He, B., Gao, B. B., Wang, K. C., and Xu, B.: Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism, Environ. Int., 139, 105558, https://doi.org/10.1016/j.envint.2020.105558, 2020.
Cheng, Y., He, K. B., Du, Z. Y., Zheng, M., Duan, F. K., and Ma, Y. L.: Humidity plays an important role in the PM2.5 pollution in Beijing, Environ. Pollut., 197, 68–75, https://doi.org/10.1016/j.envpol.2014.11.028, 2015.
Fry, J. L., Draper, D. C., Barsanti, K. C., Smith, J. N., Ortega, J., Winkler, P. M., Lawler, M. J., Brown, S. S., Edwards, P. M., Cohen, R. C., and Lee, L.: Secondary Organic Aerosol Formation and Organic Nitrate Yield from NO3 Oxidation of Biogenic Hydrocarbons, Environ. Sci. Technol., 48, 11944–11953, https://doi.org/10.1021/es502204x, 2014.
Grinsztajn, L., Oyallon, E., and Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data?, Adv. Neur. In., 35, 507–520, 2022.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Hanna, S., Russell, A., Wilkinson, J., Vukovich, J., and Hansen, D.: Monte Carlo estimation of uncertainties in BEIS3 emission outputs and their effects on uncertainties in chemical transport model predictions, J. Geophys. Res.-Atmos., 110, D01302, https://doi.org/10.1029/2004JD004986, 2005.
Hou, L. L., Dai, Q. L., Song, C. B., Liu, B. W., Guo, F. Z., Dai, T. J., Li, L. X., Liu, B. S., Bi, X. H., Zhang, Y. F., and Feng, Y. C.: Revealing Drivers of Haze Pollution by Explainable Machine Learning, Environ. Sci. Tech. Let., 9, 112–119, https://doi.org/10.1021/acs.estlett.1c00865, 2022.
Hu, J., Wang, Y., Ying, Q., and Zhang, H.: Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China, Atmos. Environ., 95, 598–609, 2014.
Hu, J., Chen, J., Ying, Q., and Zhang, H.: One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system, Atmos. Chem. Phys., 16, 10333–10350, https://doi.org/10.5194/acp-16-10333-2016, 2016.
Hu, J., Huang, L., Chen, M., Liao, H., Zhang, H., Wang, S., Zhang, Q., and Ying, Q.: Premature mortality attributable to particulate matter in China: source contributions and responses to reductions, Environ. Sci. Technol., 51, 9950–9959, 2017a.
Hu, J., Wang, P., Ying, Q., Zhang, H., Chen, J., Ge, X., Li, X., Jiang, J., Wang, S., Zhang, J., Zhao, Y., and Zhang, Y.: Modeling biogenic and anthropogenic secondary organic aerosol in China, Atmos. Chem. Phys., 17, 77–92, https://doi.org/10.5194/acp-17-77-2017, 2017b.
Huang, Z., Hu, Y., Zheng, J., Yuan, Z., Russell, A. G., Ou, J., and Zhong, Z.: A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM(2.5) Simulations, Environ. Sci. Technol., 51, 3852–3859, https://doi.org/10.1021/acs.est.6b05479, 2017.
Huang, Z., Zheng, J., Ou, J., Zhong, Z., Wu, Y., and Shao, M.: A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models, Environ. Sci. Technol., 53, 3110–3118, https://doi.org/10.1021/acs.est.8b06326, 2019.
Ke, G. L., Meng, Q., Finley, T., Wang, T. F., Chen, W., Ma, W. D., Ye, Q. W., and Liu, T. Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree, 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, 4–9 December, WOS:000452649403021, 2017.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwx150, 2017.
Li, T., Zhang, Q., Peng, Y., Guan, X., Li, L., Mu, J., Wang, X., Yin, X., and Wang, Q.: Contributions of Various Driving Factors to Air Pollution Events: Interpretability Analysis from Machine Learning Perspective, Environ. Int., 107861, https://doi.org/10.1016/j.envint.2023.107861, 2023.
Liang, F., Xiao, Q., Huang, K., Yang, X., Liu, F., Li, J., Lu, X., Liu, Y., and Gu, D.: The 17-y spatiotemporal trend of PM2.5 and its mortality burden in China, P. Natl. Acad. Sci. USA, 117, 25601–25608, 2020.
Liang, W., Luo, S., Zhao, G., and Wu, H.: Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms, Mathematics, 8, 765, https://doi.org/10.3390/math8050765, 2020.
Liu, J. and Xing, J.: Identifying Contributors to PM2.5 Simulation Biases of Chemical Transport Model Using Fully Connected Neural Networks, J. Adv. Model. Earth Sy., 15, e2021MS002898, https://doi.org/10.1029/2021MS002898, 2022.
Liu, J., Ding, J., Rexiding, M., Li, X., Zhang, J., Ran, S., Bao, Q., and Ge, X.: Characteristics of dust aerosols and identification of dust sources in Xinjiang, China, Atmos. Environ., 262, 118651, https://doi.org/10.1016/j.atmosenv.2021.118651, 2021a.
Liu, J., Chu, B., Chen, T., Zhong, C., Liu, C., Ma, Q., Ma, J., Zhang, P., and He, H.: Secondary organic aerosol formation potential from ambient air in Beijing: effects of atmospheric oxidation capacity at different pollution levels, Environ. Sci. Technol., 55, 4565–4572, 2021b.
Liu, S., Xing, J., Sahu, S. K., Liu, X., Liu, S., Jiang, Y., Zhang, H., Li, S., Ding, D., Chang, X., and Wang, S.: Wind-blown dust and its impacts on particulate matter pollution in Northern China: current and future scenarios, Environ. Res. Lett., 16, 114041, https://doi.org/10.1088/1748-9326/ac31ec, 2021.
Liu, X., Shen, G., Chen, L., Qian, Z., Zhang, N., Chen, Y., Chen, Y., Cao, J., Cheng, H., Du, W., Li, B., Li, G., Li, Y., Liang, X., Liu, M., Lu, H., Luo, Z., Ren, Y., Zhang, Y., Zhu, D., and Tao, S.: Spatially Resolved Emission Factors to Reduce Uncertainties in Air Pollutant Emission Estimates from the Residential Sector, Environ. Sci. Technol., 55, 4483–4493, https://doi.org/10.1021/acs.est.0c08568, 2021.
Liu, X., Lu, D., Zhang, A., Liu, Q., and Jiang, G.: Data-driven machine learning in environmental pollution: Gains and problems, Environ. Sci. Technol., 56, 2124–2133, 2022.
Loyola-González, O., Ramírez-Sáyago, E., and Medina-Pérez, M. A.: Towards improving decision tree induction by combining split evaluation measures, Knowl.-Based Syst., 277, 110832, https://doi.org/10.1016/j.knosys.2023.110832, 2023.
Ma, J., Shen, J., Wang, P., Zhu, S., Wang, Y., Wang, P., Wang, G., Chen, J., and Zhang, H.: Modeled changes in source contributions of particulate matter during the COVID-19 pandemic in the Yangtze River Delta, China, Atmos. Chem. Phys., 21, 7343–7355, https://doi.org/10.5194/acp-21-7343-2021, 2021.
Meng, C., Cheng, T. H., Gu, X. F., Shi, S. Y., Wang, W. N., Wu, Y., and Bao, F. W.: Contribution of meteorological factors to particulate pollution during winters in Beijing, Sci. Total Environ., 656, 977–985, https://doi.org/10.1016/j.scitotenv.2018.11.365, 2019.
NCEP, F.: National Centers for Environmental Prediction/National Weather Service/NOAA/US Department of Commerce, NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, 2000, updated daily.
Pleim, J. and Ran, L.: Surface flux modeling for air quality applications, Atmosphere, 2, 271–302, 2011.
Qiao, X., Ying, Q., Li, X., Zhang, H., Hu, J., Tang, Y., and Chen, X.: Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented Community Multiscale Air Quality model, Sci. Total Environ., 612, 462–471, https://doi.org/10.1016/j.scitotenv.2017.08.272, 2018.
Qu, Y., Voulgarakis, A., Wang, T., Kasoar, M., Wells, C., Yuan, C., Varma, S., and Mansfield, L.: A study of the effect of aerosols on surface ozone through meteorology feedbacks over China, Atmos. Chem. Phys., 21, 5705–5718, https://doi.org/10.5194/acp-21-5705-2021, 2021.
Ryu, Y.-H. and Min, S.-K.: Improving Wet and Dry Deposition of Aerosols in WRF-Chem: Updates to Below-Cloud Scavenging and Coarse-Particle Dry Deposition, J. Adv. Model. Earth Sy., 14, e2021MS002792, https://doi.org/10.1029/2021MS002792, 2022.
Shang, D., Peng, J., Guo, S., Wu, Z., and Hu, M.: Secondary aerosol formation in winter haze over the Beijing-Tianjin-Hebei Region, China, Front. Env. Sci. Eng., 15, 1–13, 2021.
Shen, H., Luo, Z., Xiong, R., Liu, X., Zhang, L., Li, Y., Du, W., Chen, Y., Cheng, H., Shen, G., and Tao, S.: A critical review of pollutant emission factors from fuel combustion in home stoves, Environ. Int., 157, 106841, https://doi.org/10.1016/j.envint.2021.106841, 2021.
Shu, Q., Murphy, B., Schwede, D., Henderson, B. H., Pye, H. O. T., Appel, K. W., Khan, T. R., and Perlinger, J. A.: Improving the particle dry deposition scheme in the CMAQ photochemical modeling system, Atmos. Environ., 289, 119343, https://doi.org/10.1016/j.atmosenv.2022.119343, 2022.
Stirnberg, R., Cermak, J., Kotthaus, S., Haeffelin, M., Andersen, H., Fuchs, J., Kim, M., Petit, J.-E., and Favez, O.: Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning, Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021, 2021.
Sun, H., Shin, Y. M., Xia, M., Ke, S., Wan, M., Yuan, L., Guo, Y., and Archibald, A. T.: Spatial Resolved Surface Ozone with Urban and Rural Differentiation during 1990–2019: A Space–Time Bayesian Neural Network Downscaler, Environ. Sci. Technol., 56, 7337–7349, 2021.
Sun, X., Liu, M., and Sima, Z.: A novel cryptocurrency price trend forecasting model based on LightGBM, Financ. Res. Lett., 32, 101084, https://doi.org/10.1016/j.frl.2018.12.032, 2020.
US EPA Office of Research and Development: CMAQv5.0.2 (5.0.2), Zenodo [code], https://doi.org/10.5281/zenodo.1079898, 2014.
Wang, P., Qiao, X., and Zhang, H.: Modeling PM2.5 and O3 with aerosol feedbacks using WRF/Chem over the Sichuan Basin, southwestern China, Chemosphere, 254, 126735, https://doi.org/10.1016/j.chemosphere.2020.126735, 2020.
Wang, S., Wang, P., Qi, Q., Wang, S., Meng, X., Kan, H., Zhu, S., and Zhang, H.: Improved estimation of particulate matter in China based on multisource data fusion, Sci. Total Environ., 161552, https://doi.org/10.1016/j.scitotenv.2023.161552, 2023a.
Wang, S., Wang, P., Zhang, R., Meng, X., Kan, H., and Zhang, H.: Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020, Chemosphere, 330, 138742, https://doi.org/10.1016/j.chemosphere.2023.138742, 2023b.
Wang, S., Zhang, M., Gao, Y., Wang, P., and Zhang, H.: Diagnosing drivers of PM2.5 simulation biases from meteorology, chemical composition, and emission sources using an efficient machine learning method, Zenodo [code and data set], https://doi.org/10.5281/zenodo.10283739, 2023c.
Wang, S. Y., Zhang, Y. L., Ma, J. L., Zhu, S. Q., Shen, J. Y., Wang, P., and Zhang, H. L.: Responses of decline in air pollution and recovery associated with COVID-19 lockdown in the Pearl River Delta, Sci. Total Environ., 756, 143868, https://doi.org/10.1016/j.scitotenv.2020.143868, 2021.
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y.: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees, Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, 2020.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021a.
Wei, J., Li, Z., Pinker, R. T., Wang, J., Sun, L., Xue, W., Li, R., and Cribb, M.: Himawari-8-derived diurnal variations in ground-level PM2.5 pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM), Atmos. Chem. Phys., 21, 7863–7880, https://doi.org/10.5194/acp-21-7863-2021, 2021b.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
World Health Organization: WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary, World Health Organization, Geneva, PMID: 3466200, 2021.
Wu, K., Yang, X. Y., Chen, D., Gu, S., Lu, Y. Q., Jiang, Q., Wang, K., Ou, Y. H., Qian, Y., Shao, P., and Lu, S. H.: Estimation of biogenic VOC emissions and their corresponding impact on ozone and secondary organic aerosol formation in China, Atmos. Res., 231, 104656, https://doi.org/10.1016/j.atmosres.2019.104656, 2020.
Wu, Y., Liu, J., Zhai, J., Cong, L., Wang, Y., Ma, W., Zhang, Z., and Li, C.: Comparison of dry and wet deposition of particulate matter in near-surface waters during summer, PloS one, 13, e0199241, https://doi.org/10.1371/journal.pone.0199241, 2018.
Xiao, Q., Zheng, Y., Geng, G., Chen, C., Huang, X., Che, H., Zhang, X., He, K., and Zhang, Q.: Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018, Atmos. Chem. Phys., 21, 9475–9496, https://doi.org/10.5194/acp-21-9475-2021, 2021.
Xiao, Q., Geng, G., Xue, T., Liu, S., Cai, C., He, K., and Zhang, Q.: Tracking PM2.5 and O3 Pollution and the Related Health Burden in China 20132020, Environ. Sci. Technol., 56, 6922–6932, https://doi.org/10.1021/acs.est.1c04548, 2022.
Xu, Q., Wang, S., Jiang, J., Bhattarai, N., Li, X., Chang, X., Qiu, X., Zheng, M., Hua, Y., and Hao, J.: Nitrate dominates the chemical composition of PM2.5 during haze event in Beijing, China, Sci. Total Environ., 689, 1293–1303, 2019.
Xue, T., Zheng, Y., Tong, D., Zheng, B., Li, X., Zhu, T., and Zhang, Q.: Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations, Environ. Int., 123, 345–357, 2019.
Yan, J., Xu, Y., Cheng, Q., Jiang, S., Wang, Q., Xiao, Y., Ma, C., Yan, J., and Wang, X.: LightGBM: accelerated genomically designed crop breeding through ensemble learning, Genome Biol., 22, 1–24, 2021.
Yang, W., Li, J., Wang, W., Li, J., Ge, M., Sun, Y., Chen, X., Ge, B., Tong, S., Wang, Q., and Wang, Z.: Investigating secondary organic aerosol formation pathways in China during 2014, Atmos. Environ., 213, 133–147, https://doi.org/10.1016/j.atmosenv.2019.05.057, 2019.
Yang, X., Zhao, C. F., Guo, J. P., and Wang, Y.: Intensification of aerosol pollution associated with its feedback with surface solar radiation and winds in Beijing, J. Geophys. Res.-Atmos., 121, 4093–4099, https://doi.org/10.1002/2015jd024645, 2016.
Ye, X., Wang, X., and Zhang, L.: Diagnosing the Model Bias in Simulating Daily Surface Ozone Variability Using a Machine Learning Method: The Effects of Dry Deposition and Cloud Optical Depth, Environ. Sci. Technol., 56, 16665–16675, https://doi.org/10.1021/acs.est.2c05712, 2022.
Ying, Q., Wu, L., and Zhang, H.: Local and inter-regional contributions to PM2.5 nitrate and sulfate in China, Atmos. Environ., 94, 582–592, 2014.
Ying, Q., Li, J., and Kota, S. H.: Significant contributions of isoprene to summertime secondary organic aerosol in eastern United States, Environ. Sci. Technol., 49, 7834–7842, 2015.
Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, https://doi.org/10.5194/acp-19-11031-2019, 2019.
Zhang, H., Surratt, J. D., Lin, Y. H., Bapat, J., and Kamens, R. M.: Effect of relative humidity on SOA formation from isoprene/NO photooxidation: enhancement of 2-methylglyceric acid and its corresponding oligoesters under dry conditions, Atmos. Chem. Phys., 11, 6411–6424, https://doi.org/10.5194/acp-11-6411-2011, 2011.
Zhang, H., Li, J., Ying, Q., Yu, J. Z., Wu, D., Cheng, Y., He, K., and Jiang, J.: Source apportionment of PM2.5 nitrate and sulfate in China using a source-oriented chemical transport model, Atmos. Environ., 62, 228–242, 2012.
Zhang, Q., Quan, J., Tie, X., Li, X., Liu, Q., Gao, Y., and Zhao, D.: Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China, Sci. Total Environ., 502, 578–584, 2015.
Zhang, R., Sun, X. S., Shi, A. J., Huang, Y. H., Yan, J., Nie, T., Yan, X., and Li, X.: Secondary inorganic aerosols formation during haze episodes at an urban site in Beijing, China, Atmos. Environ., 177, 275–282, https://doi.org/10.1016/j.atmosenv.2017.12.031, 2018.
Zhao, B., Liou, K.-N., Gu, Y., Li, Q., Jiang, J. H., Su, H., He, C., Tseng, H.-L. R., Wang, S., Liu, R., Qi, L., Lee, W.-L., and Hao, J.: Enhanced PM2.5 pollution in China due to aerosol-cloud interactions, Sci. Rep.-UK, 7, 4453, https://doi.org/10.1038/s41598-017-04096-8, 2017.
Zhu, Q., Bi, J., Liu, X., Li, S., Wang, W., Zhao, Y., and Liu, Y.: Satellite-based long-term spatiotemporal patterns of surface ozone concentrations in China: 2005–2019, Environ. Health Persp., 130, 027004, https://doi.org/10.1289/EHP9406, 2022.
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Numerical models are widely used in air pollution modeling but suffer from significant biases....
