WRF-CMAQ Model Research Articles

Impact of Change in Meteorological Conditions on PM2.5 Air Quality Improvement in Beijing-Tianjin-Hebei Region Using Process Analysis

The air quality has significantly improved since the implementation of the Air Pollution Prevention and Control Action Plan in 2013. The effect of meteorological conditions on air quality improvement is complex, including affecting the emission, transport, diffusion, chemical transformation, and other processes of air pollutants. Based on the WRF-CMAQ model and process analysis （PA） tool, this study evaluates the impact of meteorological factor changes on the improvement of PM2.5 concentration in the Beijing-Tianjin-Hebei Region from 2013 to 2020 and further analyzes the role of meteorological conditions on the diffusion, transport, and transformation of atmospheric pollutants. The results provided the technical support for improving air quality in China. The changed meteorological conditions resulted in the PM2.5 concentration decreasing by 5.4 μg·m-3 during the 2013-2017 period and increasing by 11.6 μg·m-3 from 2017-2020. From 2013 to 2017, aerosol chemical processes, vertical transmission, and horizontal transmission processes were the main processes affecting the PM2.5 improvement. Conversely, the change in meteorological conditions contributed little to the reduction of PM2.5 level, which is mainly affected by aerosol chemical processes, horizontal transmission, and vertical transmission.

Modeling Analysis of Nocturnal Nitrate Formation Pathways during Co-Occurrence of Ozone and PM2.5 Pollution in North China Plain

The rapid formation of secondary nitrate (NO3−) contributes significantly to the nocturnal increase of PM2.5 and has been shown to be a critical factor for aerosol pollution in the North China Plain (NCP) region in summer. To explore the nocturnal NO3− formation pathways and the influence of ozone (O3) on NO3− production, the WRF-CMAQ model was utilized to simulate O3 and PM2.5 co-pollution events in the NCP region. The simulation results demonstrated that heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) accounts for 60% to 67% of NO3− production at night (22:00 to 05:00) and is the main source of nocturnal NO3−. O3 enhances the formation of NO3 radicals, thereby further promoting nocturnal N2O5 production. In the evening (20:00 to 21:00), O3 sustains the formation of hydroxyl (OH) radicals, resulting in the reaction between OH radicals and nitrogen dioxide (NO2), which accounts for 48% to 64% of NO3− formation. Our results suggest that effective control of O3 pollution in NCP can also reduce NO3− formation at night.

Source Attribution Analysis of an Ozone Concentration Increase Event in the Main Urban Area of Xi’an Using the WRF-CMAQ Model

Source Attribution Analysis of an Ozone Concentration Increase Event in the Main Urban Area of Xi’an Using the WRF-CMAQ Model

Enhancing real-time PM2.5 forecasts: A hybrid approach of WRF-CMAQ model and CNN algorithm

As fine particulate matter (PM2.5) poses significant environmental and human health risks, there is an urgent need for accurate forecasting systems. In Taiwan, the current air quality forecasting (AQF) system based on the Weather Research and Forecasting meteorological model and Community Multiscale Air Quality model provides essential predictions but is limited by biases and computational complexities. This study introduces a convolutional neural network (CNN)-based PM2.5 forecasting model to enhance prediction accuracy. The CNN model incorporates hourly PM2.5 concentrations from surface observations and the AQF system, along with synoptic weather patterns (SWPs), to predict PM2.5 levels up to 72 h in advance. Three CNN models were developed: CNN-BASE (without SWPs), CNN-SWP (with SWPs), and CNN-SWPW (with SWPs and a weighted loss function). Performance assessment reveals a significant reduction in the mean RMSE of 72-h PM2.5 prediction, from 10.48 μg/m3 with the AQF system to 6.88 μg/m3 with the CNN-BASE model. However, CNN-BASE showed the lowest prediction accuracy for high PM2.5 concentrations (only 26.2%) due to a small subset of samples. Including SWPs improves the model's ability to capture meteorological influences, enhancing predictions of high PM2.5 concentrations. Furthermore, CNN-SWPW incorporates a weighted loss function to address imbalanced sample size distributions, further enhancing the accuracy of high PM2.5 predictions. This study demonstrates the potential of CNNs in operational air quality forecasting.

Uncertainties of biogenic VOC emissions caused by land cover data and implications on ozone mitigation strategies for the Yangtze river Delta region

Biogenic volatile organic compounds (BVOC) play a crucial role in ground-level ozone formation, yet emission quantification faces considerable uncertainties stemming from input data. Land cover data, which determines the distribution of growth forms, is an essential driving input of BVOC emissions. This study explores the uncertainty in BVOC emission estimates arising from using two land cover datasets, specifically the MODIS and ESA land cover data, and the implications for ozone mitigation strategies in the Yangtze River Delta (YRD) region. By employing the MEGAN model for the year 2021, we estimated that ESA land cover data, with a finer 10 m resolution, yields a total BVOC emission estimate of 2299 Gg, which is over 52.9% higher than the MODIS dataset with a resolution of 500 m, attributed to the higher tree coverage (33.9% in ESA vs. 17.6% in MODIS) identified in the ESA dataset. We then simulated the ozone concentrations by both emission estimates with the WRF-CMAQ model. The elevated BVOC emissions based on the ESA data improved the underestimation of simulated isoprene concentrations and consequently resulted in more than 30% higher domain-averaged MDA8 ozone concentration compared to the MODIS-derived BVOC emission. Regarding ozone mitigation strategy, simulation with the ESA-based BVOC emissions results in 0.3–1.2 μg/m3 less ozone reductions compared to that with the MODIS-based emissions when reducing anthropogenic VOC emissions, but 0.7–2.9 μg/m3 higher ozone reductions when controlling anthropogenic NOx emissions. These disparities are more pronounced at the city level, highlighting the importance of accurate BVOC emission estimates for effective ozone control strategies.

Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model

Analysis of the Causes of an O3 Pollution Event in Suqian on 18–21 June 2020 Based on the WRF-CMAQ Model

Source apportionment and formation of warm season ozone pollution in Chengdu based on CMAQ-ISAM

In this study, the WRF-CMAQ model integrated with BEM urban canopy model was used to simulate the concentrations of Ozone (O3) and its precursors, NOx and VOCs, in warm season of Chengdu, conduct source apportionment and formation analysis. The results show that the O3 in Chengdu exhibits a west-high/east-low spatial pattern, attributable to nearly 40% contribution from boundary sources representing the transport role of the Sichuan Basin, regional sources from districts emitting high precursor concentrations, and increasing biogenic contributions from western areas due to rising BVOCs emissions during the warm season. NOx from traffic and VOCs from industrial sources, both prevalent in Chengdu's high urban density areas, chemically react to form O3, making these sectors primary contributors to O3. NOx photochemical reactions producing O3 occur at 150 m–2500 m with peak generation rates of 10 μg/(m3·hr). Ground-level NO titration removal is most significant during heavy traffic (14:00–21:00), ranging from −70 to −200 μg/(m3·hr). O3 is replenished through similar rates of daytime vertical diffusion and nighttime horizontal advection, correlating with urban density across regions. Controlling Chengdu's warm season O3 requires focusing on long-distance external transport and regional precursor emission reductions, with strategies tailored to local urban characteristics.

Impact of Meteorological Conditions on PM2.5 Pollution in Changchun and Associated Health Risks Analysis

The escalating concern regarding increasing air pollution and its impact on the health risks associated with PM2.5 in developing countries necessitates attention. Thus, this study utilizes the WRF-CMAQ model to simulate the effects of meteorological conditions on PM2.5 levels in Changchun, a typical city in China, during January 2017 and January 2020. Additionally, it introduces a novel health risk-based air quality index (NHAQI) to assess the influence of meteorological parameters and associated health risks. The findings indicate that in January 2020, the 2-m temperature (T2), 10-m wind speed (WS10), and planetary boundary layer height (PBLH) were lower compared to those in 2017, while air pressure exhibited a slight increase. These meteorological parameters, characterized by reduced wind speed, heightened air pressure, and lower boundary layer height—factors unfavorable for pollutant dispersion—collectively contribute to the accumulation of PM2.5 in the atmosphere. Moreover, the NHAQI proves to be more effective in evaluating health risks compared to the air quality index (AQI). The annual average decrease in NHAQI across six municipal districts from 2017 to 2020 amounts to 18.05%. Notably, the highest health risks are observed during the winter among the four seasons, particularly in densely populated areas. The pollutants contributing the most to the total excess risk (ERtotal) are PM2.5 (45.46%), PM10 (33.30%), and O3 (13.57%) in 2017, and PM2.5 (67.41%), PM10 (22.32%), and O3 (8.41%) in 2020. These results underscore the ongoing necessity for PM2.5 emission control measures while emphasizing the importance of considering meteorological parameters in the development of PM2.5 reduction strategies.

Impacts of transportation emissions on horizontal and vertical distributions of air pollutants in Shanghai: Insights from emission reduction in COVID-19 lockdown

Transportation is a major sector of anthropogenic emissions in urban areas and deteriorates air quality. The surface and vertical observational data were combined with the model results to reveal its impact on the horizontal and vertical variations of pollutants during the COVID-19 lockdown period. The evident reductions in ambient PM2.5 (∼30%) and NO2 (∼50%) concentrations but a ∼25% increase in O3 concentration were observed at the transportation sites. On the vertical scale, a uniform decrease of ∼28% in PM2.5 concentrations was observed within 600 m. However, the vertical profiles of NO2 and O3 exhibited increasing vertical variation rates with concentrations varying significantly within 400 m. Meanwhile, Ox shared a similar pattern of vertical profile with O3, with a uniform increase (∼5%) within 600 m in the urban area. The WRF-CMAQ model reproduced the variations, and revealed that the reduction of transportation emissions was the key factor contributing to the increase of urban O3 and Ox due to the weakened NO titration effect. The simulated vertical profile of NO2 was featured by a decreasing curve, while that of O3 exhibited the opposite trend. We find that the transportation emissions impact vertical concentrations of NO2 and O3 within at most 400 m. The process analysis revealed that the vertical transport and horizontal transport from bay areas contributed to O3 in the urban area, while chemical processes mainly consumed it. The reduction in transportation emissions weakened the consumption and resulted in O3 accumulation during rush hours and at night. The variation of planetary boundary layer height also favored the rise of urban O3 by promoting vertical transport at daytime and trapping it at night. The reduction in NOx emissions from the transportation enhanced O3 pollution, suggesting that collaborative reductions in VOCs from multiple sectors should be conducted. This study also indicated that regional collaborations in emission reductions were necessary for comprehensive air pollution prevention.

Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China

A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.

Unraveling the influence of biogenic volatile organic compounds and their constituents on ozone and SOA formation within the Yellow River Basin, China

Biogenic volatile organic compounds (BVOC) assume a pivotal role during the formation stages of ozone (O3) and secondary organic aerosols (SOA), serving as their primary precursors. We used the latest MEGAN3.1 model, updated vegetation data and emission factors, combined with MODIS data analysis to simulate and estimate the integrated emissions of BVOC from nine provinces in China's Yellow River Basin in 2018. Following an extensive evaluation of the WRF-CMAQ model utilizing diverse parameters, the simulated and observed values had correlation coefficients between them that ranged from 0.94 to 0.99, implying a favorable outcome in terms of simulation efficacy. The findings from the simulation analysis reveal that the combined BVOC emissions from the nine provinces in the Yellow River Basin reached a total of 6.51 Tg in 2018. Among these provinces, Sichuan, Henan, and Shaanxi ranked highest, with emissions of 1.28 Tg, 1.04 Tg, and 0.96 Tg, respectively. BVOC emissions led to concentrations of 36.72 μg/m³ in the daily maximum 8-h ozone and 0.59 μg/m³ in the average SOA in nine provinces of the Yellow River Basin in July. Isoprene contributed the most to the O3 production with 6.31 μg/m3, and monoterpenes contributed the most to SOA production with 0.45 μg/m3. ΔSOA and ΔOzone are mainly distributed in the belts of central Sichuan Province, southern Shaanxi Province, western Henan Province, northern Qinghai Province, central Inner Mongolia, and southern Shanxi Province, and most of these areas are located 50 km around the Yellow River. O3 and SOA in Taiyuan, Xi'an, Chengdu, and Zhengzhou cities are strongly influenced by the generation of BVOCs. This study provides a reliable scientific basis for the prevention and control of air pollution in the Yellow River Basin.

Impacts of Anthropogenic Emission Reduction on Urban Atmospheric Oxidizing Capacity During the COVID-19 Lockdown

In recent years， regional compound air pollution events caused by fine particles （PM2.5） and ozone （O3） have occurred frequently in economically developed areas of China， in which atmospheric oxidizing capacity （AOC） has played an important role. In this study， the WRF-CMAQ model was used to study the impacts of anthropogenic emission reduction on AOC during the COVID-19 lockdown period. Three representative cities in eastern China （Shijiazhuang， Nanjing， and Guangzhou） were selected for an in-depth analysis to quantify the contribution of meteorology and emissions to the changes in AOC and oxidants and to discuss the impact of AOC changes on the formation of secondary pollutants. The results showed that， compared with that in the same period in 2019， the urban average AOC in Shijiazhuang， Nanjing， and Guangzhou in 2020 increased by 60%， 48.7%， and 12.6%， respectively. The concentrations of O3， hydroxyl radical （·OH）， and nitrogen trioxide （NO3·） increased by 1.6%-26.4%， 14.8%-73.3%， and 37.9%-180%， respectively. The AOC in the three cities increased by 0.06×10-4， 0.12×10-4， and 0.33×10-4 min-1， respectively， due to emission reduction. The meteorological change increased AOC in Shijiazhuang and Nanjing by 20% and 17.9%， respectively， but decreased AOC in Guangzhou by -9.3%. Enhanced AOC led to an increase in the nitrogen oxidation ratio （NOR） and VOCs oxidation ratio （VOR） and promoted the transformation of primary pollutants to secondary pollutants. This offset the effects of primary emission reduction and resulted in a nonlinear decline in secondary pollutants compared to emissions during the COVID-19 lockdown.

Comprehensive evaluation of air pollution emission permit allocation: Effectiveness, efficiency, and equity in China's environmental management framework

Though the emission permit system is a pivotal policy in China's environmental management, there's a lack of comprehensive assessments for air pollutant emission quota allocation. In this study, a comprehensive 3E integrated assessment approach, encompassing the dimensions of Effectiveness, Efficiency, and Equity, was employed to thoroughly evaluate methods for allocating air pollution emission permits. This analysis was based on plant-level data derived from 9181 industrial enterprises situated in seven major cities within the Beijing-Tianjin-Hebei region of China during the year 2017. The Effectiveness assessment encompassed the utilization of the WRF-CMAQ model for simulating air pollution concentrations of PM2.5, SO2, and NO2. The findings underscored the imperative need for intensified efforts to control PM2.5 and NO2 emissions, particularly in achieving compliance with the national annual average level II thresholds. Efficiency assessment navigated the delicate balance between environmental benefits and costs, with a particular emphasis on health benefits and expenses associated with pollution abatement. The results revealed annual net benefits ranging from 8.4 to 19.9 billion yuan, illuminating a highly favorable cost-benefit equilibrium. Equity assessment delved into the examination of the equitable distribution of resources and impacts. Utilizing metrics of Environmental Gini Coefficient (EGC) and Green Contribution Coefficient (GCC), the analysis uncovered disparities in most scenarios and cities, shedding light on areas necessitating targeted policy interventions. Overall, the integrated 3E assessment offers a holistic comprehension of policy impacts. Based on the overall 3E weighted scores, results showed that the allocation method that considered both the emission standard and the pollutant transmission (Scenario S2) outperformed among the three designed allocation methods. In the future, policy efforts should focus on enhancing comprehensive assessments, ensuring the sustainable development and betterment of environmental quality through air pollution control measures.

Analysis of O3 Sources in Yulin City in Summer Based on WRF-CMAQ/ISAM Model

In order to have a clearer understanding of the sources of ozone pollution in Yulin City in summer and put forward scientific governance suggestions, the WRF-CMAQ model was used to simulate the O3 concentration in Yulin City and surrounding areas (including Taiyuan City, Xi'an City, Yinchuan City, Hohhot City, and other provincial capital cities) in July 2019. Using the ISAM module, the sources of O3 and its precursors NOx and VOCs in a heavy pollution process in Yulin City were quantified. The results showed that on heavy pollution days, the O3 in Yulin City mainly came from the long-distance transmission outside the simulation area (55.5%), followed by the photochemical reaction of precursors in the simulation area (20.6%, 10.0%, 5.0%, 2.3%, and 2.1%, respectively, in Yulin City, Shanxi Province, Inner Mongolia Autonomous Region, and Shaanxi Province, 1.2% in Gansu Province, Ningxia Hui Autonomous Region, and Henan Province in total), and initial conditions (0.3%); the remaining sources (23.6%) could not be successfully labeled. Yulin City is in the VOCs control area, and its VOCs were composed of paraffin (76.5%), ketones (9.2%), and other types of VOCs (14.3%). The VOCs came from the emission of pollution sources in the simulation area (45.6%, 22.0%, 11.4%, 6.3%, and 5.1%, respectively, in Yulin City, Shanxi Province, Inner Mongolia Autonomous Region, and Shaanxi Province, 0.8% in Gansu Province, Ningxia Hui Autonomous Region, and Henan Province in total) and the long-distance transmission outside the simulation area (27.9%); the remaining 26.5% were not successfully marked. This research showed that to control the O3 pollution in Yulin, not only should the local VOCs emissions be controlled, but the overall planning of VOCs emissions in the peripheral areas should also be done well.

Decoupling in the vertical shape of HCHO during a sea breeze event: The effect on trace gas satellite retrievals and column-to-surface translation

The effect of sea breeze circulation on stratification of the vertical formaldehyde (HCHO) concentration vertical profiles is explored using a regional atmospheric chemical transport model (CTM) for three synoptically stagnant days focused on the east coast of the U.S in June 2018. During this event, a significant thermal contrast between the Atlantic Ocean and the terrestrial regions (12–17 °C), observed by moderate resolution imaging spectroradiometer (MODIS) and well-captured by the WRF-CMAQ model (15–18 °C), is conducive to monsoon-like flow, perpendicular to the shorelines, carrying clean marine air masses over the land within a few hundreds of meters above the surface. In contrast, the westerly continental polluted air masses prevail in higher altitudes. These two conflicting flows result in atypical vertical shapes of HCHO concentrations increasing with altitude. This decoupling pattern is so pronounced that we observe total column HCHO negatively correlate with surface concentrations. Comparisons of an accredited global model, GEOS-CF, to surface wind measurements and MODIS skin temperature indicate its poor representation of the sea breeze timing and strength, resulting in GEOS-CF HCHO vertical shapes being drastically different from the WRF-CMAQ. Based on radiative transfer calculations, the differences in the vertical distribution of HCHO between the WRF-CMAQ and that of GEOS-CF in the first 3 km are sufficient to induce a 20–30% error in air mass factors (thus total vertical HCHO column abundances). Through an experiment involving converting HCHO total columns to surface mixing ratios, we demonstrate that GEOS-CF allocates noticeably more HCHO molecules (40–150%) to the surface layer due to the misrepresentation of the vertical shape of HCHO during the sea breeze event. It is known that a significant fraction of the human population lives in coastal areas prone to detrimental effects caused by air pollution, and elevated pollutant concentrations usually occur in synoptically stagnant atmospheric conditions when local circulation patterns come into play; accordingly, our experiments emphasize the importance of the effect a priori profiles can have on satellite-derived applications under such conditions. To ensure that the quantitative representation of satellite-based trace gas retrievals on a daily basis is trustworthy and useable for air quality applications, atmospheric models providing a priori profiles for satellite retrievals should be well-tuned to reproduce complex local circulation such as sea-land breezes.

A WRF-CMAQ modeling of atmospheric peroxyacetyl nitrate and source apportionment in Central China

Atmospheric peroxyacetyl nitrate (PAN), as an essential constituent in the photochemical smog, is formed from photochemical reactions between volatile organic compounds (VOCs) and NOx. However, limited regional studies on distribution, formation and sources of PAN restrict the further understanding of the atmospheric behavior and environmental significance of PAN. In this study, the variation characteristics of PAN and the influencing factors to PAN concentrations were investigated using the WRF-CMAQ model simulation in the central China during July 2019. The results showed that the monthly mean concentration of PAN in the near-surface layer was 0.4 ppbv and increased with the height rising, accompanied by strong intra-day variation. The process analysis suggested that the removal was mainly controlled by dry deposition (57 %), followed by the gas-phase chemistry (43 %) which was mainly attributed to the thermal decomposition. Based on the sensitivity simulation, PAN concentrations decreased effectively in most of the simulated regions when precursors of VOCs and NOx emissions were reduced, and PAN concentrations were more sensitive to VOCs emissions than NOx emissions. The reduction of NOx and VOCs could lead to enhanced atmospheric oxidation in east-central region, which in turn hindered the decrease of PAN concentrations. During the simulation period, we found that emissions from industry and transportation sectors had the greatest impact on PAN concentrations in the central China, with contributions of 39 %–49 % and 33 %–41 %, respectively.

A quantitative assessment and process analysis of the contribution from meteorological conditions in an O3 pollution episode in Guangzhou, China

Ozone plays a significant role in the troposphere due to its influence on human health, air pollution, and global warming. Despite the decline of NOx and VOCs in China in recent years, obvious increases in O3 concentration took place, while the attributions to it remained unclear. Previous studies have revealed that the contribution of meteorological conditions is getting increasingly significant to O3 concentration. Given that O3 concentration continued to climb in China, the influence of meteorology on O3 concentration should not be neglected. In this study, the WRF-CMAQ model was utilized to study an O3 episode in the Pearl River Delta (PRD) region, China. In this short-term simulation, anthropogenic emissions remained unchanged during the Polluted Week (PW) compared with the Clean Week (CW), which allowed us to investigate the effects of changes in meteorological conditions (ΔMET) on O3 variations via an efficient method of sensitivity experiment. And the contribution of changes in anthropogenic emissions (ΔEMIS) was evaluated similarly. It turns out that ΔMET explained 85.8% of the elevated O3 concentration in urban Guangzhou (GZ), which resulted from an increase in temperature and solar radiation, and a decrease in wind speed and humidity, while ΔEMIS explained 13.3%. We also analyzed the 3-dimensional development process of the O3 pollution and quantitatively assessed it by process analysis in the CMAQ model. Meteorology affected O3 concentration through both chemical reactions and physical processes, with the former contributing 36.9% and the latter 63.1%. Most of the elevated O3 was found near the surface at a height less than 50 m above the ground, which accounted for 56.8%. This study emphasized the contribution of meteorological conditions to O3 concentration and demonstrated a streamlined method of sensitivity experiment to quantitatively identify the impact of meteorology, which could be useful for O3 prevention and emissions reduction strategies.

Rice yield losses due to O3 pollution in China from 2013 to 2020 based on the WRF-CMAQ model

High ground-level ozone (O3) concentrations has been shown to inhibit crops growth and decrease their yields. Current indicators for assessing the impact of O3 on crop yields are usually based on the ambient concentration, without considering recently suggested flux-based indicators (especially stomatal flux) for ecological risk assessment. This study applied the WRF-CMAQ model system, the parameterized Jarvis stomatal conductance model, and the O3 flux model to simulate the changes of O3 uptake flux in rice stomata in major rice growing areas in China during 2013–2020. The yield losses of rice caused by O3 pollution were estimated using both exposure-based (AOT40) and flux-based (PODγ) indicators. The rice yield losses experienced an overall increasing and decreasing trend in 2013–2018 and 2018–2020, respectively. The yield losses estimated by AOT40 were 3.5–5.9% for single rice, 1.8–3.5% for double-early rice, and 2.4–5.4% for double-late rice during 2013–2020, while the losses based on PODγ were 5.1–13.0%, 2.1–10.6%, and 6.7–15.9%, respectively. The yearly rice yield losses based on AOT40 and PODγ were 5.9–11.4 Mt (million metric tons) and 8.0–28.0 Mt for 2013–2020, respectively. This study can provide an important reference for avoiding the impact of O3 pollution and climate change on rice yield in the future.

Drivers of 2015–2021 trends in cold winter surface PM2.5 in the Harbin-Changchun megalopolis in China: Meteorology vs. anthropogenic emission

Harbin-Changchun megalopolis (HCM) is a typical representative of the cold urban agglomeration region in China. Although the average annual PM2.5 concentration has experienced an evolutionary process continuous deterioration to gradual improvement in the past decade, but severe haze episodes still occur during cold winter periods. WRF-CMAQ model and observed dataset of surface PM2.5 concentration were conducted in this study to identify the drivers of 2015–2021 trends in cold winter surface PM2.5 in the HCM by separating contributions from anthropogenic emissions and meteorology changes. Meteorological condition change was the dominant driver of 2015–2021 trends in cold winter surface PM2.5 in the HCM. The years with most unfavorable, typical, and most favorable meteorological conditions for PM2.5 concentration reduction during January from 2015 to 2021 for the whole HCM region were 2020, 2017, and 2018, respectively. The major controlling meteorological factors that affect the surface PM2.5 concentration change were relative humidity, maximum wind direction and speed in cold winter in the HCM. In addition, we found that the emission cut policy began to play a practical role in the air quality improvement in the HCM from year of 2018. The emission reduction during the 2017–2021 resulted in an approximately 19% (15.7 μg/m3) decrease of PM2.5 concentrations in January. Furthermore, sensitivity experiments demonstrated that residential emission cut was the key measures for future air quality improvement in cold winter in the HCM.

Why did air quality experience little improvement during the COVID-19 lockdown in megacities, northeast China?

To inhibit the COVID-19 (Coronavirus disease 2019) outbreak, unprecedented nationwide lockdowns were implemented in China in early 2020, resulting in a marked reduction of anthropogenic emissions. However, reasons for the insignificant improvement in air quality in megacities of northeast China, including Shenyang, Changchun, Jilin, Harbin, and Daqing, were scarcely reported. We assessed the influences of meteorological conditions and changes in emissions on air quality in the five megacities during the COVID-19 lockdown (February 2020) using the WRF-CMAQ model. Modeling results indicated that meteorology contributed a 14.7% increment in Air Quality Index (AQI) averaged over the five megacities, thus, the local unfavorable meteorology was one of the causes to yield little improved air quality. In terms of emission changes, the increase in residential emissions (+15%) accompanied by declining industry emissions (−15%) and transportation (−90%) emissions resulted in a slight AQI decrease of 3.1%, demonstrating the decrease in emissions associated with the lockdown were largely offset by the increment in residential emissions. Also, residential emissions contributed 42.3% to PM2.5 concentration on average based on the Integrated Source Apportionment tool. These results demonstrated the key role residential emissions played in determining air quality. The findings of this study provide a scenario that helps make appropriate emission mitigation measures for improving air quality in this part of China.