Impacts of model resolution on predictions of air quality and associated health exposure in Nanjing, China

Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method

Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 μg m-3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 μg m-3 and 0.96 μg m-3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

Insights into the source contributions to the elevated fine particulate matter in Nigeria using a source-oriented chemical transport model

In 2021, Nigeria was ranked by the World Health Organization (WHO) as one of the top countries with highly deteriorating air quality in the world. To date, no study has elucidated the sources of elevated fine particulate matter (PM2.5) concentrations over the entire Nigeria. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to quantify the contributions of seven emissions sectors to PM2.5 and its components in Nigeria in 2021. Residential, industry, and agriculture were the major sources of primary PM (PPM) during the four seasons, elemental carbon (EC) and primary organic carbon (POC) were dominated by residential and industry, while residential, industry, transportation, and agriculture were the important sources of secondary inorganic aerosols (SIA) and its components in most regions. PM2.5 was up to 150 μg/m3 in the north in all the seasons, while it reached ∼80 μg/m3 in the south in January. Residential contributed most to PM2.5 (∼80 μg/m3), followed by industry (∼40 μg/m3), transportation (∼20 μg/m3), and agriculture (∼15 μg/m3). The large variation in the sources of PM2.5 and its components across Nigeria suggests that emissions control strategies should be separately designed for different regions. The results imply that urgent control of PM2.5 pollution in Nigeria is highly necessitated.

A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: Application, evaluation and uncertainty factors

With the gradual deepening of the research and governance of air pollution, chemical transport models (CTMs), especially the third-generation CTMs based on the "1 atm" theory, have been recognized as important tools for atmospheric environment research and air quality management. In this review article, we screened 2396 peer-reviewed manuscripts on the application of four pre-selected regional CTMs in the past five years. CAMx, CMAQ, WRF-Chem and NAQPMS models are well used in the simulation of atmospheric pollutants. In the simulation study of secondary pollutants such as O3, secondary organic aerosol (SOA), sulfates, nitrates, and ammonium (SNA), the CMAQ model has been widely applied. Secondly, model evaluation indicators are diverse, and the establishment of evaluation criteria has gone through the long-term efforts of predecessors. However, the model performance evaluation system still needs further specification. Furthermore, temporal-spatial resolution, emission inventory, meteorological field and atmospheric chemical mechanism are the main sources of uncertainty, and have certain interference with the simulation results. Among them, the inventory and mechanism are particularly important, and are also the top priorities in future simulation research.

Association of exposure to ozone and fine particulate matter with ovarian reserve among women with infertility

Evidence linking diminished ovarian reserve, a significant cause of female infertility, and exposure to particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5) or O3 exposure remains a critical knowledge gap in female fertility. This study investigated the association between ambient PM2.5, O3 pollution, and anti-Müllerian hormone (AMH), a sensitive marker of ovarian reserve, in reproductive-aged Chinese women. We enrolled 2212 women with spontaneous menstrual cycles who underwent AMH measurements at a reproductive medicine center between 2018 and 2021. The daily mean concentrations of outdoor PM2.5 and O3 were estimated using a validated spatiotemporal model, followed by matching the participants' residential addresses. Three exposure periods were designed according to AMH expression patterns during follicle development. A generalized linear model was used to investigate changes in AMH associated with air pollution. The results showed a mean AMH level of 3.47 ± 2.61 ng/mL. During the six months from primary to early antral follicle stage (Period 1), each 10 μg/m3 increase in PM2.5 and O3 exposure was associated with AMH changes of -0.21 (95% confidence interval [CI]: -0.48, 0.06) ng/mL and -0.31 (95% CI: -0.50, -0.12) ng/mL, respectively. Further analyses indicated that the reduced ovarian reserve measured by AMH level was only significantly associated with PM2.5 exposure during follicle development from the primary to preantral follicle stage (Period 2) but was significantly associated with O3 exposure during Periods 1, 2, and 3. These observations were robust in the dual-pollutant model considering co-exposure to PM2.5 and O3. The results indicated an inverse association between ovarian reserve and ambient O3 exposure and suggested distinct susceptibility windows for O3 and PM2.5 for reduced ovarian reserve. These findings highlight the need to control ambient air pollution to reduce invisible risks to women's fertility, especially at high O3 concentrations.

Improving the accuracy of O3 prediction from a chemical transport model with a random forest model in the Yangtze River Delta region, China

Due to inherent errors in the chemical transport models, inaccuracies in the input data, and simplified chemical mechanisms, ozone (O₃) predictions are often biased from observations. Accurate O₃ predictions can better help assess its impacts on public health and facilitate the development of effective prevention and control measures. In this study, we used a random forest (RF) model to construct a bias-correction model to correct the bias in the predictions of hourly O₃ (O₃-1h), daily maximum 8-h O₃ (O₃-Max8h), and daily maximum 1-h O₃ (O₃-Max1h) concentrations from the Community Multi-Scale Air Quality (CMAQ) model in the Yangtze River Delta region. The results show that the RF model successfully captures the nonlinear response relationship between O₃ and its influence factors, and has an outstanding performance in correcting the bias of O₃ predictions. The normalized mean biases (NMBs) of O₃-1h, O₃-Max8h, and O₃-Max1h decrease from 15.8%, 20.0%, and 17.0.% to 0.5%, -0.8%, and 0.1%, respectively; correlation coefficients increase from 0.78, 0.90, and 0.89 to 0.94, 0.95, and 0.94, respectively. For O₃-1h and O₃-Max8h, the original CMAQ model shows an obvious bias in the central and southern Zhejiang region, while the RF model decreases the NMB values from 54% to -1% and 34% to -4%, respectively. The O₃-1h bias is mainly caused by the bias of nitrogen dioxide (NO₂). Relative humidity and temperature are also important factors that lead to the bias of O₃. For high O₃ concentrations, the temperature bias and O₃ observations are the major reasons for the discrepancy between the model and the observations.

Diagnostic analysis of regional ozone pollution in Yangtze River Delta, China: A case study in summer 2020

A regional ozone (O₃) pollution event occurred in the Yangtze River Delta region during August 17-23, 2020 (except on August 21). This study aims to understand the causes of O₃ pollution during the event using an emission-based model (i.e., the Community Multiscale Air Quality (CMAQ) model) and an observation-based model (OBM). The OBM was used to investigate O₃ sensitivity to its precursors during the O₃ pollution, concluding that O₃ formation was limited by volatile organic compounds (VOCs) on August 19, but was co-limited by VOCs and nitrogen oxides (NOx) on other polluted days. Aromatics and alkenes were the two main VOC groups contributing to the O₃ formation, with trans-2-butene and m/p-xylene as the key species among the VOCs measured at the Nanjing urban site. The source apportionment results estimated using the source-oriented CMAQ model suggest that the transportation and industry sources dominated the non-background O₃ production in Nanjing, which were responsible for 52% and 24.7%, respectively. The O₃ concentration attributed to NOx (~70%) was significantly higher than that attributed to VOCs (approximately 30%). The process analysis revealed that vertical mixing increased the O₃ concentrations in the early morning, and photochemical reactions promoted O₃ formation and accumulation during the daytime within the planetary boundary layer. At night, outflow from horizontal transport and nocturnal chemistry jointly resulted the O₃ depletion. The contributions of inter-city transport during the O₃ pollution period in Nanjing were also estimated. The predicted O₃ concentration was largely recorded from long-distance regions, reaching 46%, followed by local sources (38%) and surrounding cities (16%). The results indicate that both NOx and VOCs contributed significantly to O₃ pollution during this event, and the emissions controls of NOx and the key VOC species of aromatics and alkenes from a cooperative regional perspective should be considered to mitigate O₃ pollution.

Implications of Mitigating Ozone and Fine Particulate Matter Pollution in the Guangdong-Hong Kong-Macau Greater Bay Area of China Using a Regional-To-Local Coupling Model

Ultrahigh-resolution air quality models that resolve sharp gradients of pollutant concentrations benefit the assessment of human health impacts. Mitigating fine particulate matter (PM2.5) concentrations over the past decade has triggered ozone (O3) deterioration in China. Effective control of both pollutants remains poorly understood from an ultrahigh-resolution perspective. We propose a regional-to-local model suitable for quantitatively mitigating pollution pathways at various resolutions. Sensitivity scenarios for controlling nitrogen oxide (NOx) and volatile organic compound (VOC) emissions are explored, focusing on traffic and industrial sectors. The results show that concurrent controls on both sectors lead to reductions of 17%, 5%, and 47% in NOx, PM2.5, and VOC emissions, respectively. The reduced traffic scenario leads to reduced NO2 and PM2.5 but increased O3 concentrations in urban areas. Guangzhou is located in a VOC-limited O3 formation regime, and traffic is a key factor in controlling NOx and O3. The reduced industrial VOC scenario leads to reduced O3 concentrations throughout the mitigation domain. The maximum decrease in median hourly NO2 is >11 μg/m³, and the maximum increase in the median daily maximum 8-hr rolling O3 is >10 μg/m³ for the reduced traffic scenario. When controls on both sectors are applied, the O3 increase reduces to <7 μg/m³. The daily averaged PM2.5 decreases by <2 μg/m³ for the reduced traffic scenario and varies little for the reduced industrial VOC scenario. An O3 episode analysis of the dual-control scenario leads to O3 decreases of up to 15 μg/m³ (8-hr metric) and 25 μg/m³ (1-hr metric) in rural areas.

Novel Method for Ozone Isopleth Construction and Diagnosis for the Ozone Control Strategy of Chinese Cities

Ozone (O₃) isopleths describe the nonlinear responses of O₃ concentrations to changes in nitrogen oxides (NO_X) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O₃ control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O₃ pollution of China in 2017 and derive O₃ isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O₃ pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O₃ as 160 μg·m^-3 (81.5 ppbV equally). The O₃ responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NO_X-saturated regimes, where a small amount of NO_X reduction increases O₃. Ambient O₃ pollution in the eastern region generally is limited by VOCs, while in the west by NO_X. The city-specific O₃ isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O₃ abatement in China.

Does ozone inhalation cause adverse metabolic effects in humans? A systematic review

We utilized a practical, transparent approach for systematically reviewing a chemical-specific evidence base. This approach was used for a case study of ozone inhalation exposure and adverse metabolic effects (overweight/obesity, Type 1 diabetes [T1D], Type 2 diabetes [T2D], and metabolic syndrome). We followed the basic principles of systematic review. Studies were defined as "Suitable" or "Supplemental." The evidence for Suitable studies was characterized as strong or weak. An overall causality judgment for each outcome was then determined as either causal, suggestive, insufficient, or not likely. Fifteen epidemiologic and 33 toxicologic studies were Suitable for evidence synthesis. The strength of the human evidence was weak for all outcomes. The toxicologic evidence was weak for all outcomes except two: body weight, and impaired glucose tolerance/homeostasis and fasting/baseline hyperglycemia. The combined epidemiologic and toxicologic evidence was categorized as weak for overweight/obesity, T1D, and metabolic syndrome,. The association between ozone exposure and T2D was determined to be insufficient or suggestive. The streamlined approach described in this paper is transparent and focuses on key elements. As systematic review guidelines are becoming increasingly complex, it is worth exploring the extent to which related health outcomes should be combined or kept distinct, and the merits of focusing on critical elements to select studies suitable for causal inference. We recommend that systematic review results be used to target discussions around specific research needs for advancing causal determinations.

Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM_2.5) and ozone (O₃) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM_2.5 and O₃ in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM_2.5 and O₃ using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM_2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O₃-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM_2.5-related mortality, and up to 30% for the O₃-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 10³ people/km², mostly located in Central China, for both PM_2.5 and O₃. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.

Premature mortality attributable to PM2.5 pollution in China during 2008-2016: Underlying causes and responses to emission reductions

Long-term exposure to fine particulate matter (PM_2.5) poses a great threat to public health in China. To this end, the Chinese government promulgated the Air Pollution Prevention and Control Action Plan (the Action Plan) in 2013. However, the health benefits of the Action Plan have not been well explained. In this paper, the underlying causes of changes in premature mortality attributable to PM_2.5 pollution and the response of this mitigation policy in China were explored using sensitivity analysis. The simulated annual average PM_2.5 concentration reduced by 24.9% over mainland China from 2008 to 2016. Subsequently, national premature mortality would decrease by 14.4% from 1.14 million (95% CI: 0.54, 1.55) in 2008 to 0.98 million (95% CI: 0.44, 1.38) in 2016. Specifically, premature mortality reduced by 209,600 cases (-18.3%) owing to PM_2.5 reduction during 2008-2016, of which 188,500 cases were from 2014 to 2016 due to the Action Plan in 2013. Note that the health benefits were limited when compared with air quality improvements, mainly due to that the IER functions have a stable curve at higher concentration intervals. Meanwhile, premature mortality would have increased by 14.2% from 2008 to 2016 owing to demographic changes, substantially weakening the impact of the decrease in PM_2.5 and baseline mortality. The effectiveness of China's new air pollution mitigation policy was proved through the research. However, considering the non-linear response of mortality to PM_2.5 changes and the aggravation of demography trends, stronger emission control steps should be further taken to protect public health in China.