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Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

Many studies in China investigated how the lockdown following the coronavirus disease 2019 substantially affected air quality; however, few were conducted in Northeastern China. Here, the changes in six criteria air pollutants, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3), were investigated in Shenyang from January to May 2015-2020. Compared with the pre-lockdown, the mass concentrations of PM2.5, PM10, SO2, NO2, and CO during the lockdown decreased by 40.3% to 48.6%, indicating a positive impact of lockdown policies on reducing pollutant emissions. The responses of PM2.5, PM10, and CO to the lockdown measures in downtown areas were more sensitive than in the suburbs. However, the O3 concentration showed the opposite trend, attributed to the drop in NOx and particulate matters. Compared to the same period in 2015-2019, the proportion of days with good air quality increased from 63.2% to 77.2% during the lockdown and Shenyang experienced no severe pollution. Our results suggest that reducing human activities can improve air quality; however, coordinated control policies of O3, PM2.5, and NO2 are imperative. [ABSTRACT FROM AUTHOR]

Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emissions, which has led to rapid improvement in air quality over China. Here, we evaluated the impact of anthropogenic emissions and meteorological conditions on trends in air pollutants in a coastal city (Lianyungang) in eastern China from 2015 to 2022 based on a random forest model. The annual mean concentration of observed air pollutants, including fine particles, inhalable particles, sulfur dioxide, nitrogen dioxide, and carbon monoxide, presented significant decreasing trends during 2015–2022, with dominant contributions (55–75%) by anthropogenic emission reduction. An increasing trend in ozone was observed with an important contribution (28%) by anthropogenic emissions. The impact of meteorological conditions on air pollution showed significant seasonality. For instance, the negative impact on aerosol pollution occurred during cold months, while the positive impact was in warm months. Health-risk-based air quality decreased by approximately 40% in 8 years, for which anthropogenic emission made a major contribution (93%). [ABSTRACT FROM AUTHOR]

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The "coal to gas" policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the "coal to gas" policy on air quality. Strong evidence shows that the "coal to gas" policy has significantly improved the air quality in Beijing. On average, the "coal to gas" policy reduced sulfur dioxide ( SO 2 ), nitrogen dioxide ( NO 2 ), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the "coal to gas" policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the "coal to gas" policy, so as to alleviate the air pollution in Beijing, China. [ABSTRACT FROM AUTHOR]

Summary: We hypothesized that air pollution could cause oxidative damage and inflammation in the human body, which was linked to bone loss. Our result showed that long-term exposure to air pollution might decrease bone mineral density (BMD) T-score and increase the prevalence of osteoporosis in Hubei province. Introduction: Osteoporosis is becoming an increasingly serious public health problem with the advent of global aging. Long-term exposure to air pollution has been linked to multitudinous adverse health outcomes, but evidence is still relatively limited and inconsistent for BMD T-score and osteoporosis. This study aimed at exploring the associations between long-term exposure to air pollution and BMD T-score and osteoporosis. Methods: The Hubei part of the China Osteoporosis Prevalence Study was extracted. Data on air pollutants were collected by the national air quality real-time release platform of China Environmental Monitoring Station. Linear mixed models and multilevel logistic regression analyses were performed to assess the associations between air pollution and BMD T-score and osteoporosis, respectively. Subgroup analyses were conducted to identify vulnerable populations. Results: A total of 1845 participants were included in this cross-section study. Per 10 ug/m3 increase in PM2.5 and SO2 were associated with 0.20 (95% CI: 0.04, 0.36) and 0.31 (95% CI: 0.11, 0.51) decrease in BMD T-score of the neck of femur, respectively. Per 10 ug/m3 increase in CO was linked with 0.03 (95% CI: 0.02, 0.05) decrease in BMD T-score of the total hip. Per 1 ug/m3 increase in PM2.5 was associated with 5% increase in the prevalence of osteoporosis in all participants. In general, the higher concentrations of PM2.5 with the more adverse effect on osteoporosis (P for trend = 0.01). The impact of PM2.5 on osteoporosis in males was higher than that in females [1.29, 95% CI (1.11, 1.50) vs 1.01, 95% CI (0.95, 1.07)]. Per 1 ug/m3 increase in PM10 corresponded with 4% elevation in the risks of osteoporosis in rural population. The ORs (95% CI) for the association of osteoporosis and NO2 in ever/current smoking and drinking population were 1.07 (1.01, 1.13) and 1.05 (1.00, 1.09), respectively. SO2 had a statistically significant positive effect on people with comorbidity [OR = 1.10, (95% CI: 1.00 to 1.21)], while none in people without comorbidity [OR = 0.96, (95% CI: 0.88 to 1.05)]. Conclusion: Our study provided evidence that long-term exposure to PM2.5 was linked with the decreased BMD T-score and increased risk of osteoporosis among all participants. The adverse impacts of PM2.5, PM10, and NO2 were larger in males than in females. People having comorbidity, living in rural areas, and current/ever smoking or drinking were more vulnerable to air pollution. Public health departments should consider air pollution to formulate better preventive measures for osteoporosis. [ABSTRACT FROM AUTHOR]

The long-range transport of biomass burning pollutants from Southeast Asia has a significant impact on air quality in China. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) fire data and aerosol optical depth (AOD) products and the Tropospheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data were used to analyze the impact of air pollution caused by biomass burning in Southeast Asia on southern China. Results showed that Yunnan, Guangdong and Guangxi were deeply affected by biomass burning emissions from March to April during 2016–2020. Comparing the data for fires on the Indochinese Peninsula and southern provinces of China, it is obvious that the contribution of pollutants emitted by local biomass burning in China to air pollution is only a small possibility. The distribution of CO showed that the overall emissions increased greatly from March to April, and there was an obvious transmission process. In addition, the MODIS AOD in areas close to the national boundary of China is at a high level (>0.6), and the AOD in the southwest of Guangxi province and the southeast of Yunnan Province is above 0.8. Combined with a typical air pollution event in southern China, the UVAI combined with wind direction and other meteorological data showed that the pollutants were transferred from the Indochinese Peninsula to southern China under the southwest monsoon. The PM2.5 data from ground-based measurements and backward tracking were used to verify the pollutant source of the pollution event, and it was concluded that the degree of pollution in Yunnan, Guangxi and Guangdong provinces was related to the distance from the Indochinese Peninsula. Results indicate that it is necessary to carry out in-depth research on the impact of cross-border air pollution transport on domestic air quality as soon as possible and to actively cooperate with foreign countries to carry out pollution source research and control. [ABSTRACT FROM AUTHOR]

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants' (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China. [ABSTRACT FROM AUTHOR]

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China has implemented active clean air policies in recent years, and the spatiotemporal patterns of major pollutant emissions have changed substantially. In this study, we construct a regional air pollution data assimilation system based on the WRF/CMAQ model and ensemble Kalman filter algorithm to quantitatively optimize gridded CO emissions using hourly surface CO measurements over China. The Multi‐resolution Emission Inventory of China CO emission inventories in December 2012 and 2016 are treated as prior emissions, and the CO emissions in December 2013 and 2017 are optimized using the CO observations of December of 2013 and 2017, respectively. The results show that in both periods, assimilation of CO observations significantly improves the CO simulations and emission estimates. Assimilation increases the CO emissions in most areas of mainland China, especially in northern China, and the spatial patterns of the increases in the two periods are similar. Overall, the posterior CO emissions in December 2017 are 17% lower than those in December 2013. Large emission decreases are mainly found in most key urban areas and developed regions, and emission increases are mainly located in their surrounding areas and certain central and western regions, which might reflect the emission migration from developed regions or urban areas to developing regions or surrounding areas. These changes are not found in the prior emissions but are basically consistent with the emission control strategies and industrial transformation and upgrade phenomena in recent years, indicating that our CO assimilation system could successfully capture the temporal and spatial variations. Plain Language Summary: CO emissions over China in December 2013 and 2017 were optimized using surface hourly CO observations, and the emission changes between these two periods were presented. We found that there were large emission decreases in key urbans and developed regions, and increases in their surrounding areas and some central and western regions, which were basically consistent with the emission control strategies and industrial transformation in recent years, might reflect the emission migration. Key Points: A regional air pollution data assimilation system based on WRF/CMAQ and EnKF is constructedOverall, the CO emissions are reduced by 17% between December 2013 and 2017 over the whole mainland of ChinaThe emissions migration is found in the posterior emissions but not shown in the prior emissions [ABSTRACT FROM AUTHOR]