Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020)

Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China

Elucidating the characteristics and influencing mechanisms of PM_2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM_2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM_2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM_2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM_2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM_2.5 concentrations present a U-shaped distribution. The PM_2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM_2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM_2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM_2.5 concentrations in winter. The effect of anthropogenic emissions on PM_2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM_2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM_2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM_2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Isotopic imprints of aerosol ammonium over the north China plain

Atmospheric PM_2.5 poses a variety of health and environmental risks to urban environments. Ammonium is one of the main components of PM_2.5, and its role in PM_2.5 pollution will likely increase in the coming years as NH₃ emissions are still unregulated and rising in many cities worldwide. However, partitioning urban NH₄⁺ sources remains challenging. Although the ¹⁵N natural abundance (δ¹⁵N) analysis is a promising approach for this purpose, it has seldom been applied across multiple cities within a given region. This limits our understanding of the regional patterns and controls of NH₄⁺ sources in urban environments. Here, we collected PM_2.5 samples using an active sampling technique during winter at six cities in the North China Plain to characterize the concentrations, δ¹⁵N and sources of NH₄⁺ in PM_2.5. We found substantial variations in both the concentrations and δ¹⁵N of NH₄⁺ among the sites. The mean NH₄⁺ concentrations across the six cities ranged from 3.6 to 12.1 μg m^-3 on polluted days and from 0.9 to 10.6 μg m^-3 on non-polluted days. The δ¹⁵N ranged from 6.5‰ to 13.9‰ on polluted days and from 8.7‰ to 13.5‰ on non-polluted days. The δ¹⁵N decreased with increasing NH₄⁺ concentrations at all six sites. We found that non-agricultural sources (vehicle exhaust, ammonia slip and urban wastes) contributed 72%-94% and 56%-86% of the NH₄⁺ on polluted and non-polluted days, respectively, and that during polluted days, combustion-related emissions (vehicle exhaust and ammonia slip) were positively associated with the proportion of urban area, population density and number of vehicles, highlighting the importance of local sources of particulate pollution. This study suggests that the analysis of ¹⁵N in aerosol NH₄⁺ is a promising approach for apportioning atmospheric NH₃ sources over a large region, and this approach has potential for mapping rapidly and precisely the sources of NH₃ emissions.

Effects of urban form on air quality: A case study from China comparing years with normal and reduced human activity due to the COVID-19 pandemic

This study explored the dynamic and complex relationships between air quality and urban form when considering reduced human activities. Applying the random forest method to data from 62 prefecture-level cities in China, urban form-air quality relationships were compared between 2015 (a normal year) and 2020 (which had significantly reduced air pollution due to COVID-19 lockdowns). Significant differences were found between these two years; urban compactness, shape, and size were of prime importance to air quality in 2020, while fragmentation was the most critical factor in improving air quality in 2015. An important influence of traffic mode was also found when controlling air pollution. In general, in the pursuit of reducing air pollution across society, the best urban forms are continuous and compact with reasonable building layouts, population, and road densities, and high forest area ratios. A polycentric urban form that alleviates the negative impacts of traffic pollution is preferable. Urban development should aim to reduce air pollution, and optimizing the effects of urban form on air quality is a cost-effective way to create better living environments. This study provides a reference for decision-makers evaluating the effects of urban form on air pollution emission, dispersion, and concentration in the post-pandemic era.

Health Risk Assessment of Inhalation Exposure to Airborne Particle-Bound Nitrated Polycyclic Aromatic Hydrocarbons in Urban and Suburban Areas of South China

Airborne particulates (PM_2.5 and TSP) were collected from outdoor and indoor areas at urban (Haizhu District) and suburban (Huadu District) sites from 2019 to 2020 in Guangzhou. Three nitro-polycyclic aromatic hydrocarbons (nitro-PAHs) in the airborne particulates were identified by a gas chromatograph equipped with a triple-quadrupole mass spectrometer. In the Haizhu District and Huadu District, the nitro-PAH concentrations in PM_2.5 and TSP did not show a significant decrease from winter to summer. From 2019 to 2020, the difference in the average concentration of nitro-PAHs in PM_2.5 and TSP in Guangzhou was relatively low and had no statistical significance. The diagnostic ratios of 2-nitrofluorene (2-NF)/1-nitropyrene (1-NP) in TSP are less than five, while for 2-NF/1-NP in outdoor PM_2.5 in the summer of 2019 and 2020 are more than five, which indicates that nitro-PAHs in the atmospheric PM_2.5 in Guangzhou during summer mainly originated from the secondary formation of atmospheric photochemical reactions between parent PAHs and oxidants (·OH, NO₃, and O₃). 9-Nitroanthracene (9-NT) made the most significant contribution to the total nitro-PAH concentration. The incremental lifetime cancer risks (ILCRs) of nitro-PAHs in PM_2.5 and TSP by inhalation exposure indicated low potential health risks in the urban-suburban of Guangzhou.

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.

Data-driven predictive modeling of PM2.5 concentrations using machine learning and deep learning techniques: a case study of Delhi, India

The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM_2.5 concentration at a location in Delhi. For this purpose, multi-layer feed-forward neural network (MLFFNN), support vector machine (SVM), random forest (RF) and long short-term memory networks (LSTM) have been applied. The air pollutants, e.g., CO, Ozone, PM₁₀, NO, NO₂, NO_x, NH₃, SO₂, benzene, toluene, as well as meteorological parameters (temperature, wind speed, wind direction, rainfall, evaporation, humidity, pressure, etc.), have been used as inputs in the present study. Moreover, this is one of the first papers that employ aerodynamic roughness coefficient as an input parameter for the prediction of PM_2.5 concentration. The result of the study shows that the LSTM model with index of agreement (IA) 0.986, root mean square error (RMSE) 21.510, Nash-Sutcliffe efficiency index (NSE) 0.945, (coefficient of determination)R² 0.945, and (correlation coefficient)R 0.972 is the best performing technique for the prediction of PM_2.5 followed by MLFFNN, SVM, and RF models. The sensitivity analysis for the LSTM model reported that PM₁₀, wind speed, NH₃, and benzene are the key influencing parameters for the estimation of PM_2.5. The findings in this work suggest that the LSTM could advance in PM_2.5 forecasting and thus would be useful for developing fine-scale, state-of-the-art air pollution forecasting models.

Observed sensitivities of PM2.5 and O3 extremes to meteorological conditions in China and implications for the future

Frequent extreme air pollution episodes in China accompanied with high concentrations of particulate matters (PM2.5) and ozone (O3) are partly supported by meteorological conditions. However, the relationships between meteorological variables and pollution extremes can be poorly estimated solely based on mean pollutant level. In this study, we use quantile regression to investigate meteorological sensitivities of PM2.5 and O3 extremes, benefiting from nationwide observations of air pollutants over 2013-2019 in China. Results show that surface winds and humidity are identified as key drivers for high PM2.5 events during both summer and winter, with greater sensitivities at higher percentiles. Higher humidity favors the hydroscopic growth of particles during winter, but it tends to decrease PM2.5 through wet scavenging during summer. Surface temperature play dominant role in summer O3 extremes, especially in VOC-limited regime, followed by surface winds and radiation. Sensitivities of O3 to meteorological conditions are relatively unchanging across percentiles. Under the fossil-fueled development pathway (SSP5-8.5) scenario, meteorological conditions are projected to favor winter PM2.5 extremes in North China Plain (NCP), Yangtze River Delta (YRD) and Sichuan Basin (SCB), mainly due to enhanced surface specific humidity. Summer O3 extremes are likely to occur more frequently in the NCP and YRD, associated with warmer temperature and stronger solar radiation. Besides, meteorological conditions over a relatively longer period play a more important role in the formation of pollution extremes. These results improve our understanding of the relationships between extreme PM2.5 and O3 pollution and meteorology, and can be used as a valuable reference of model predicted air pollution extremes.

Tropical modulation of East Asia air pollution

Understanding air pollution in East Asia is of great importance given its high population density and serious air pollution problems during winter. Here, we show that the day-to-day variability of East Asia air pollution, during the recent 21-year winters, is remotely influenced by the Madden-Julian Oscillation (MJO), a dominant mode of subseasonal variability in the tropics. In particular, the concentration of particulate matter with aerodynamic diameter less than 10 micron (PM10) becomes significantly high when the tropical convections are suppressed over the Indian Ocean (MJO phase 5-6), and becomes significantly low when those convections are enhanced (MJO phase 1-2). The station-averaged PM10 difference between these two MJO phases reaches up to 15% of daily PM10 variability, indicating that MJO is partly responsible for wintertime PM10 variability in East Asia. This finding helps to better understanding the wintertime PM10 variability in East Asia and monitoring high PM10 days.

Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution

PM_2.5 pollution is a complex process mainly affected by emission sources and meteorological conditions. However, it is hard to accurately assess the effects of emission sources and meteorological conditions on the variation of PM_2.5 concentrations in the complex atmospheric environment. In this study, the Random Forest model with Shapley Additive exPlanations (RF-SHAP) and Partial Dependence Plot (RF-PDP) was combined with Positive Matrix Factorization (PMF) to evaluate the impacts of various factors on PM_2.5 pollution. The results show that anthropogenic emissions and meteorological conditions contributed about 67% (40.5 μg/m³) and 33% (19.7 μg/m³) to variation in PM_2.5 concentrations, respectively. Specifically, secondary nitrate (SN) had the greatest impact among all sources (about 45%). Hence, we further explore the impacts of the primary sources and meteorological conditions on SN formation. Coal combustion and vehicle emissions significantly contribute to the formation of SN by providing a large number of precursor NO_X. Additionally, the RF-PDP method was further employed to estimate the synergistic effects of primary sources and meteorological conditions on SN formation. The results help reveal strategies to simultaneously reduce SN by controlling primary emissions under suitable meteorological conditions. This work also suggests that the machine learning model can utilize online datasets well and provide a reliable approach for analyzing the causes of PM_2.5 pollution.

Decadal changes in PM2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls

In China, the rapid development of the economy and implementation of multiple emission control policies in recent decades have been accompanied by dramatic changes in air quality. In this study, PM_2.5 concentrations estimated by using MERRA-2 reanalysis data were integrated into the Global Exposure Mortality Model (GEMM) to explore the spatiotemporal variation of nationwide PM_2.5-related premature mortality from 1990 to 2019, and the driving factors behind decadal changes were evaluated. Since 2000, as a result of PM_2.5 pollution, air quality in China has deteriorated substantially, especially in the fast-developing eastern and southern parts. In 2009, the nationwide population-weighted (PW) PM_2.5 concentration peaked at 41.4 μg/m³ (95% confidence interval [CI], 36.7-46.2). Simultaneously, the GEMM results revealed that nationwide PM_2.5-related deaths increased remarkably from 1089 (95% CI, 965-1210) thousand in 1990 to 1795 (1597-1986) thousand in 2009. The implementation of the toughest-ever Air Pollution Prevention and Control Action Plan (APPCAP) in 2013 effectively controlled PM_2.5 pollution in China. By 2018, the nationwide PW PM_2.5 concentration had decreased to 34.0 (29.2-38.9) μg/m³. Dynamic trend prediction revealed that, although the APPCAP achieved substantial health benefits, the policy did not result in further remarkable reductions in PM_2.5-related deaths; in 2019, deaths peaked at 1932 (1716-2140) thousand. PM_2.5-related deaths in 2030 were projected for each of four emission control scenarios. The results of the driving factor analysis and the future projections indicated that the health benefits from improving air quality are likely to be counterbalanced by changes in the population age structure. Because population ageing is becoming more and more rapid in China and the challenge of climate change is increasing, the results of this study imply that policymakers need to implement more stringent measures and set more ambitious emission control targets to reduce nationwide PM_2.5-related premature mortality in the future.

Numerical study of the amplification effects of cold-front passage on air pollution over the North China Plain

Cold-front systems provide scavenging mechanisms for air pollution in the North China Plain (NCP), but the transport of pollutants with cold fronts aggravates air quality downstream. The impact of cold fronts on PM_2.5 concentrations over the NCP during 8-14 December 2019 was studied using the WRF-Chem model. Results indicate that cold fronts directly influence PM_2.5 concentration through regional transport of pollutants and adjustment of meteorological systems, and they indirectly affect air quality by influencing aerosol-radiation interaction. Pollutants affecting downstream areas may be transported to altitudes of ~3 km along the frontal surface, with near-surface PM_2.5 concentrations increasing temporarily at up to 15 μg·m^-3·h^-1 behind the surface frontal line owing to the inversion layer triggered by the oblique frontal surface. The transport process plays an essential role in affecting air pollution levels, more than vertical mixing and chemical reaction processes. Changes in the meteorological system (eastward shift of the high-pressure center) occurring with the passage of cold fronts facilitate the accumulation and transport of pollutants in the NCP, reducing air quality in the western and northern NCP. Cold fronts may also indirectly exacerbate near-surface pollutant diffusion conditions by affecting solar radiation incidence, with a reduction of the 2-m temperature by as much as 1 °C, increasing near-surface (<1 and 0.5 km agl on the pre- and post-frontal sides, respectively) PM_2.5 concentrations by up to 40 μg·m^-3, while reducing upper-layer concentrations by up to 30 μg·m^-3. This study emphasizes the amplification effect of cold fronts on air pollution, with inter-regional cooperation being essential in improving air quality in the NCP region.

E-waste dismantling as a source of personal exposure and environmental release of fine and ultrafine particles

Electronic waste (WEEE; from TV screens to electric toothbrushes) is one of the fastest growing waste streams in the world. Prior to recycling, e-waste components (metals, wood, glass, etc.) are processed by shredding, grinding and chainsaw cutting. These activities generate fine and ultrafine particle emissions, containing metals as well as organics (e.g., flame retardants), which have high potential for human health impacts as well as for environmental release. In this work, release of fine and ultrafine particles, and their exposure impacts, was assessed in an e-waste recycling facility under real-world operating conditions. Parameters monitored were black carbon, particle mass concentrations, ultrafine particles, and aerosol morphology and chemical composition. Potential health impacts were assessed in terms of cytotoxicity (cell viability) and oxidative stress (ROS) on <2 μm particles collected in liquid suspension. Environmental release of WEEE aerosols was evidenced by the higher particle concentrations monitored outside the facility when compared to the urban background (43 vs.11 μgPM2.5/m³, respectively, or 2.4 vs. 0.2 μgCa/m³). Inside the facility, concentrations were higher in the top than on the ground floor (PM2.5 = 147 vs. 78 μg/m³, N = 15.4 ∗ 104 vs. 8.7 ∗ 104/cm³, BC = 12.4 vs. 7.2 μg/m³). Ventilation was a key driver of human exposure, in combination with particle emissions. Key chemical tracers were Ca (from plastic fillers) and Fe (from wiring and other metal components). Y, Zr, Cd, Pb, P and Bi were markers of cathode TV recycling, and Li and Cr of grinding activities. While aerosols did not evidence cytotoxic effects, ROS generation was detected in 4 out of the 12 samples collected, associated to the ultrafine fraction. We conclude on the need for studies on aerosol emissions from WEEE facilities, especially in Europe, due to their demonstrable environmental and human health impacts and the rapidly growing generation of this type of waste.

Catastrophic PM2.5 emissions from Siberian forest fires: Impacting factors analysis

With increased forest fires due to climate change, PM_2.5 emissions also intensified. Record PM_2.5 emissions according to Copernicus Atmosphere Monitoring Service in Russia amounted to 8 megatons (Mt) in 2021, which is 78% higher than the average level of 2004-2021 (4.5 Mt). Seven federal subjects (the constituent entities) with vast forest areas without fire protection produced 86% of emissions (6.8 Mt) in 2021, the major losses (6.1 Mt) in Yakutia (Sakha Republic). The ambient temperature in Eastern Siberia is increasing, especially in months of winter and spring seasons (up to +3.6 °C) in 1990-2020 compared to 1901-2020 (CEDA Archive); climate change has affected meteorological conditions leading to increased forest fires. The results of the SARIMAX model study for PM_2.5 emissions considering meteorological factors using ERA5 and burnt forest area using MODIS (MCD64A1), establishing a significant dependence of PM_2.5 emissions on the lack of precipitation and the associated parameters of complete and potential evaporation. This influence long before the fire season (up to 9 months), as it affects the snow cover and the dryness of the fuel by the beginning of forest fires. In turn, high PM_2.5 emission values are accompanied by a drop in 2 m air temperature and surface solar radiation downwards due to the aerosol saturation with suspended particles. The average COR for seven federal subjects was 0.79, with the highest forecast result in Yakutia (0.95), indicating the maximum propensity for record emissions due to weather conditions. In combination with forest management without fire protection, meteorological parameters have caused an increase in PM_2.5 emissions in recent years in Siberia. The forest needs other ways to manage under the pressures of climate change to reduce environmental pollution associated with PM_2.5 emissions from vast Siberian fires.

Spatiotemporal variations of PM2.5 pollution and its dynamic relationships with meteorological conditions in Beijing-Tianjin-Hebei region

Identifying the effects of meteorological conditions on PM_2.5 pollution is of great significance to explore methods to reduce atmospheric pollution. This study attempts to analyze the spatiotemporal variations of PM_2.5 pollution and its dynamic nexus with meteorological factors in the Beijing-Tianjin-Hebei (BTH) region from 2015 to 2020 using standard deviation ellipse (SDE) and panel vector autoregressive (PVAR) model. The results indicate that: (1) In 2015-2020, PM_2.5 pollution decreased significantly, indicating air pollution control policies in China have taken effect; Also, it showed a cumulative effect, or there was the path dependence of air pollution. (2) PM_2.5 pollution presented a distribution pattern from northeast to southwest, while the directionality of air pollution has weakened. Based on SDE, PM_2.5 pollution in Cangzhou can reflect the average level in the BTH; (3) Meteorological conditions exhibited a lagged and sustained effect on PM_2.5 pollution. Specifically, the effects of meteorological factors on PM_2.5 presented disequilibrium over time. In the long run, precipitation and temperature mainly showed negative impacts on PM_2.5 pollution, while wind speed, relative humidity and sunshine duration aggravated PM_2.5 pollution in the BTH. This study contributes to extending the study on the spatiotemporal evolution of PM_2.5 pollution and its links with meteorological conditions.

The relationship between short-term PM2.5 exposure and outpatient visits for acne vulgaris in Chongqing, China: a time-series study

Many researches have reported the air pollution impacts, either long term or short term, on inflammatory skin diseases, but there are few studies on the relation between PM_2.5 and acne vulgaris. To determine the correlation between short-term PM_2.5 exposure and acne outpatient visits, data for 120,842 acne vulgaris outpatient visits between December 2013 and December 2019 were obtained from three large hospitals in Chongqing, China. Both single-pollutant models and two-pollutant models were established to explore the relationship between PM_2.5 exposure and acne outpatient visits. The stratified analyses were conducted through two-sample z-tests to investigate the possible gender (male or female) and age (< 25 years or ≥ 25 years) differences in PM_2.5 effects. The results demonstrated positive correlations between PM_2.5 concentrations and acne outpatient visits. A 10 μg/m³ increase in PM_2.5 concentration was associated with a 1.71% (95% CI: 1.06-2.36%) increase in acne outpatient visits at lag 0-7 day. Stratified analyses showed that PM_2.5 effects were greater in individuals aged ≥ 25 years than those aged < 25 years, but no gender difference was found. In conclusion, short-term PM_2.5 exposure was positively associated with the risk of acne outpatient visits, especially for people ≥ 25 years old.

Migration and diffusion characteristics of air pollutants and meteorological influences in Northwest China: a case study of four mining areas

In the process of exploiting mineral resources, dust enters the environment through air suspended particles and surface runoff, which has a serious impact on the atmospheric environment and human health. From all-year and seasonal scenarios, the migration trajectories and cumulative concentration based on the secondary development of Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) in four mining areas (SF, BC, SJZ, and MJT) in Northwest China are studied. The convergent cross mapping (CCM) method is used to study the causal relationship between concentration and meteorological factors. In this process, the problem of missing non-station meteorological data is solved with the help of the inverse distance weighted interpolation method, and the problem in which the convergence requirements of the CCM algorithm cannot meet the requirements is solved with the bootstrap method. The results indicated that the short path has the characteristics of slow movement, short migration path, low altitude(< 1 km), and high contribution rate, while the long path has the opposite characteristics. Furthermore, the results demonstrated that the concentration is centered on the pollution source and diffuses around, with a diffusion radius of 220-270 km, showing a serious pollution center and slight gradient settlement on the edge, but the overall distribution of accumulated concentration is uneven. The results also show that temperature (TEMP and S_TEMP), evaporation, and air pressure are the main meteorological factors affecting the all-year concentration. The concentration and meteorological factors in the four mining areas also show significant seasonal characteristics, and the correlation in spring, summer, and autumn is stronger than that in winter. This study not only provides a reference for the green and sustainable exploitation of mineral resources but also provides theoretical support for the joint prevention and control of transboundary pollution.

The Characteristics of PM2.5 and PM10 and Elemental Carbon Air Pollution in Sevastopol, Crimean Peninsula

In most cities of the world, air pollution reaches critical levels. The air masses circulating over the Crimean Peninsula bring a significant amount of mineral dust, which contains soil particles, emissions from industrial enterprises, gases, etc. The purpose of this research is to study the processes and the factors influencing atmospheric pollution in Sevastopol (Crimea). Air pollutant concentration data, including elemental carbon, nutrients (inorganic fixed nitrogen, inorganic fixed phosphorus and silicon), PM10, and PM2.5, were collected during this research. Samples were collected at the station that is located at a distance from sources of pollution (background station). Our study has shown that even at the background site the daily-averaged concentrations of PM10 and PM2.5 particles in the atmosphere of Sevastopol reach and even exceed the maximum permissible concentrations in the case of dust transported from deserts. Values of the daily-averaged concentrations of microparticles have exceeded the European maximum permissible concentration (MPC) values in 17 cases for PM2.5 particles and in 6 cases for PM10. The impact of both local sources and long-distance atmospheric transport depends on weather conditions. Concentrations of elemental carbon in air samples have never exceeded the maximum allowed by regulations concentration limits during our research. However, the elemental carbon concentration in air samples collected near highways with a traffic intensity of approximately 500–1000 cars per hour has exceeded the background values by 30–50 times.

Determination of the human impact on the drop in NO2 air pollution due to total COVID-19 lockdown using Human-Influenced Air Pollution Decrease Index (HIAPDI)

This study investigates the relationship between territorial human influence and decreases in NO2 air pollution during a total COVID-19 lockdown in Metropolitan France. NO2 data from the confinement period and the Human Influence Index (HII) were implemented to address the problem. The relative change in tropospheric NO2 was calculated using Sentinel-5P (TROPOMI) satellite data. Hotspot-Coldspot analysis was performed to examine the change in NO2. Moreover, the novel Human-Influenced Air Pollution Decrease Index (HIAPDI) was developed. Weather bias was investigated by implementing homogeneity analysis with χ2 test. The correlations between variables were tested with the statistical T-test. Likewise, remote observations were validated with data from in-situ monitoring stations. The study showed a strong correlation between the NO2 decrease during April 2020 under confinement measures and HII. The greater the anthropogenic influence, the greater the reduction of NO2 in the regions (R2 = 0.62). The new HIAPDI evidenced the degree of anthropogenic impact on NO2 change. HIAPDI was found to be a reliable measure to determine the correlation between human influence and change in air pollution (R2 = 0.93). It is concluded that the anthropogenic influence is a determining factor in the phenomenon of near-surface NO2 reduction. The implementation of HIAPDI is recommended in the analysis of other polluting gases.

Effect of Vertical Wind Shear on PM2.5 Changes over a Receptor Region in Central China

Vertical wind shear (VWS) significantly impacts the vertical mixing of air pollutants and leads to changes in near-surface air pollutants. We focused on Changsha (CS) and Jingmen (JM), the upstream and downstream urban sites of a receptor region in central China, to explore the impact of VWS on surface PM2.5 changes using 5-year wintertime observations and simulations from 2016–2020. The surface PM2.5 concentration was lower in CS with higher anthropogenic PM2.5 emissions than in JM, and the correlation between wind speed and PM2.5 was negative for clean conditions and positive for polluted conditions in both two sites. The difference in the correlation pattern of surface PM2.5 and VWS between CS and JM might be due to the different influences of regional PM2.5 transport and boundary layer dynamics. In downstream CS, the weak wind and VWS in the height of 1–2 km stabilized the ABL under polluted conditions, and strong northerly wind accompanied by enhanced VWS above 2 km favored the long-range transport of air pollutants. In upstream JM, local circulation and long-range PM2.5 transport co-determined the positive correlation between VWS and PM2.5 concentrations. Prevailed northerly wind disrupted the local circulation and enhanced the surface PM2.5 concentrations under polluted conditions, which tend to be an indicator of regional transport of air pollutants. The potential contribution source maps calculated from WRF-FLEXPART simulations also confirmed the more significant contribution of regional PM2.5 transport to the PM2.5 pollution in upstream region JM. By comparing the vertical profiles of meteorological parameters for typical transport- and local-type pollution days, the northerly wind prevailed throughout the ABL with stronger wind speed and VWS in transport-type pollution days, favoring the vertical mixing of transported air pollutants, in sharp contrast to the weak wind conditions in local-type pollution days. This study provided the evidence that PM2.5 pollution in the Twain-Hu Basin was affected by long-distance transport with different features at upstream and downstream sites, improving the understanding of the air pollutant source–receptor relationship in air quality changes with regional transport of air pollutants.

Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics.

Causation inference in complicated atmospheric environment

Reliable attribution is crucial for understanding various climate change issues. However, complicated inner-interactions between various factors make causation inference in atmospheric environment highly challenging. Taking PM_2.5-Meteorology causation, which involves a large number of non-Linear and uncertain interactions between many meteorological factors and PM_2.5, as a case, we examined the performance of a series of mainstream statistical models, including Correlation Analysis (CA), Partial Correlation Analysis (PCA), Structural Equation Model (SEM), Convergent Cross Mapping (CCM), Partial Cross Mapping (PCM) and Geographical Detector (GD). From a coarse perspective, the Top 3 major meteorological factors for PM_2.5 in 190 cities across China extracted using different models were generally consistent. From a strict perspective, the extracted dominant meteorological factor for PM_2.5 demonstrated large model variations and shared a limited consistence. Such models as SEM and PCM, which are capable of further separating direct and indirect causation in simple systems, performed poorly to identify the direct and indirect PM_2.5-Meteorology causation. The notable inconsistence denied the feasibility of employing multiple models for better causation inference in atmospheric environment. Instead, the sole use of CCM, which is advantageous in dealing with non-linear causation and removing disturbing factors, is a preferable strategy for causation inference in complicated ecosystems. Meanwhile, given the multi-direction, uncertain interactions between many variables, we should be more cautious and less ambitious on the separation of direct and indirect causation. For better causation inference in the complicated atmospheric environment, the combination of statistical models and atmospheric models, and the further exploration of Deep Neural Network can be promising strategies.

Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China

PM_2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM_2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM_2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM_2.5 were explored. The LTX model performed well with R² = 0.88, RMSE of 8.73 μg/m³ and MAE of 5.85 μg/m³. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM_2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM_2.5 values, which exhibit similar distribution trends to those of the observed PM_2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM_2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.

An observation-based adjustment method of regional contribution estimation from upwind emissions to downwind PM2.5 concentrations

We propose a method to adjust contributions from upwind emissions to downwind PM_2.5 concentrations to account for the differences between observed and simulated PM_2.5 concentrations in an upwind area. Emissions inventories (EI) typically have a time lag between the inventory year and the release year. In addition, traditional emission control policies and social issues such as the COVID-19 pandemic cause steady or unexpected changes in anthropogenic emissions. These uncertainties could result in overestimation of the emission impacts of upwind areas on downwind areas if emissions used in modeling for the upwind areas were larger than the reality. In this study, South Korea was defined as the downwind area while other regions in Northeast Asia including China were defined as the upwind areas to evaluate applicability of the proposed adjustment method. We estimated the contribution of emissions released from the upwind areas to PM_2.5 concentrations in South Korea from 2015 to 2020 using a three-dimensional photochemical model with two EIs. In these two simulations for 2015-2020, the annual mean foreign contributions differed by 4.1-5.5 µg/m³. However, after adjustment, the differences decreased to 0.4-1.1 µg/m³. The adjusted annual mean foreign contributions were 12.7 and 8.8 µg/m³ during 2015-2017 and 2018-2020, respectively. Finally, we applied the adjustment method to the COVID-19 pandemic period to evaluate the applicability for short-term episodes. The foreign contribution of PM_2.5 during the lockdown period in China decreased by 30% after adjustment and the PM_2.5 normalized mean bias in South Korea improved from 15% to -4%. This result suggests that the upwind contribution adjustment can be used to alleviate the uncertainty of the emissions inventory used in air quality simulations. We believe that the proposed upwind contribution adjustment method can help to correctly understand the contributions of local and upwind emissions to PM_2.5 concentrations in downwind areas.

Investigation of PM2.5 pollution during COVID-19 pandemic in Guangzhou, China

The COVID-19 pandemic has raised awareness about various environmental issues, including PM2.5 pollution. Here, PM2.5 pollution during the COVID-19 lockdown was traced and analyzed to clarify the sources and factors influencing PM2.5 in Guangzhou, with an emphasis on heavy pollution. The lockdown led to large reductions in industrial and traffic emissions, which significantly reduced PM2.5 concentrations in Guangzhou. Interestingly, the trend of PM2.5 concentrations was not consistent with traffic and industrial emissions, as minimum concentrations were observed in the fourth period (3/01-3/31, 22.45 μg/m3) of the lockdown. However, the concentrations of other gaseous pollutants, e.g., SO2, NO2 and CO, were correlated with industrial and traffic emissions, and the lowest values were noticed in the second period (1/24-2/03) of the lockdown. Meteorological correlation analysis revealed that the decreased PM2.5 concentrations during COVID-19 can be mainly attributed to decreased industrial and traffic emissions rather than meteorological conditions. When meteorological factors were included in the PM2.5 composition and backward trajectory analyses, we found that long-distance transportation and secondary pollution offset the reduction of primary emissions in the second and third stages of the pandemic. Notably, industrial PM2.5 emissions from western, southern and southeastern Guangzhou play an important role in the formation of heavy pollution events. Our results not only verify the importance of controlling traffic and industrial emissions, but also provide targets for further improvements in PM2.5 pollution.

Atmospheric pollutants response to the emission reduction and meteorology during the COVID-19 lockdown in the north of Africa (Morocco)

Climate and air quality change due to COVID-19 lockdown (LCD) are extremely concerned subjects of several research recently. The contribution of meteorological factors and emission reduction to air pollution change over the north of Morocco has been investigated in this study using the framework generalized additive models, that have been proved to be a robust technique for the environmental data sets, focusing on main atmospheric pollutants in the region including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10), secondary inorganic aerosols (SIA), nom-methane volatile organic compounds and carbon monoxide (CO) from the regional air pollution dataset of the Copernicus Atmosphere Monitoring Service. Our results, indicate that secondary air pollutants (PM2.5, PM10 and O3) are more influenced by metrological factors and the other air pollutants reported by this study (NO2 and SO2). We show a negative effect for PBHL, total precipitation and NW10M on PM (PM2.5 and PM10 ), this meteorological parameters contribute to decrease in PM2.5 by 9, 2 and 9% respectively, before LCD and 8, 1 and 5% respectively during LCD. However, a positive marginal effect was found for SAT, Irradiance and RH that contribute to increase PM2.5 by 9, 12 and 18% respectively, before LCD and 17, 54 and 34% respectively during LCD. We found also that meteorological factors contribute to O3, PM2.5, PM10 and SIA average mass concentration by 22, 5, 3 and 34% before LCD and by 28, 19, 5 and 42% during LCD respectively. The increase in meteorological factors marginal effect during LCD shows the contribution of photochemical oxidation to air pollution due to increase in atmospheric oxidant (O3 and OH radical) during LCD, which can explain the response of PM to emission reduction. This study indicates that PM (PM2.5, PM10) has more controlled by SO2 due to the formation of sulfate particles especially under high oxidants level. The positive correlation between westward wind at 10 m (WW10M), Northward Wind at 10 m (NW10M) and PM indicates the implication of sea salt particles transported from Mediterranean Sea and Atlantic Ocean. The Ozone mass concentration shows a positive trend with Irradiance, Total and SAT during LCD; because temperature and irradiance enhance tropospheric ozone formation via photochemical reaction.This study shows the contribution of atmospheric oxidation capacity to air pollution change.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-022-02224-z.

Influence of number of visitors and weather conditions on airborne particulate matter mass concentrations at the Plitvice Lakes National Park, Croatia during summer and autumn

We investigated the influence of local meteorological conditions and number of visitors on ambient particulate matter (PM) mass concentrations and particle fraction ratios at the Plitvice Lakes National Park between July and October 2018. Outdoor mass concentrations of particles with aerodynamic diameters of less than 1, 2.5, and 10 μm (PM1, PM2.5, and PM10, respectively) and indoor PM1 were measured with two light-scattering laser photometers set up near the largest and most visited Kozjak Lake. Our findings suggest that the particles mainly originated from background sources, although some came from local anthropogenic activities. More specifically, increases in both indoor and outdoor mass concentrations coincided with the increase in the number of visitors. Indoor PM1 concentrations also increased with increase in outdoor air temperature, while outdoor PMs exhibited U-shaped dependence (i.e., concentrations increased only at higher outdoor air temperatures). This behaviour and the decrease in the PM1/PM2.5 ratio with higher temperatures suggests that the production and growth of particles is influenced by photochemical reactions. The obtained spectra also pointed to a daily but not to weekly periodicity of PM levels.

Seasonal trend and source identification of polycyclic aromatic hydrocarbons associated with fine particulate matters (PM2.5) in Isfahan City, Iran, using diagnostic ratio and PMF model

Particulate matters (PMs) and their associated chemical compounds such as polycyclic aromatic hydrocarbons (PAHs) are important factors to evaluate air pollution and its health impacts particularly in developing countries. Source identification of these compounds can be used for air quality management. The aim of this study was to identify the sources of PM_2.5-bound PAHs in Isfahan city, a metropolitan and industrialized area in central Iran. The PM_2.5 samples were collected at 50 sites during 1 year. Source identification and apportionment of particle-bound PAHs were carried out using diagnostic ratios (DRs) of PAHs and positive matrix factorization (PMF) model. The results showed that the concentrations of PM_2.5 ranged from 8 to 291 μg/m³ with an average of 60.2 ± 53.9 μg/m³, whereas the sum of concentrations of the 19 PAH compounds (ƩPAHs) ranged from 0.3 to 61.4 ng/m³ with an average of 4.65 ± 8.54 ng/m³. The PAH compounds showed their highest and lowest concentrations occurred in cold and warm seasons, respectively. The mean concentration of benzo[a]pyrene (1.357 ng m^-3) in December-January, when inversion occured, was higher than the Iranian national standard value showing the risk of exposure to PM_2.5-bound PAHs. Applying DRs suggested that the sources of the PAHs were mainly from fuel combustion. The main sources identified by the PMF model were gasoline combustion (23.8 to 33.1%) followed by diesel combustion (20.6 to 24.8%), natural gas combustion (9.5 to 28.4%), evaporative-uncombusted (9.5 to 23.0%), industrial activities (8.4 to 13.5%), and unknown sources (2.8 to 15.7%). It is concluded that transportation, industrial activities, and combustion of natural gas (both in residential-commercial and industrial sectors) as the main sources of PAHs in PM_2.5 should be managed in the metropolitan area, particularly in cold seasons.

Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)

The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64).

Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China

In recent years, with the continuous advancement of China's urbanization process, regional atmospheric environmental problems have become increasingly prominent. We selected 12 cities as study areas to explore the spatial and temporal distribution characteristics of atmospheric particulate matter in the region, and analyzed the impact of socioeconomic and natural factors on local particulate matter levels. In terms of time variation, the particulate matter in the study area showed an annual change trend of first rising and then falling, a monthly change trend of "U" shape, and an hourly change trend of double-peak and double-valley distribution. Spatially, the concentration of particulate matter in the central and southern cities of the study area is higher, while the pollution in the western region is lighter. In terms of social economy, PM_2.5 showed an "inverted U-shaped" quadratic polynomial relationship with Second Industry and Population Density, while it showed a U-shaped relationship with Generating Capacity and Coal Output. The results of correlation analysis showed that PM_2.5 and PM₁₀ were significantly positively correlated with NO₂, SO₂, CO and air pressure, and significantly negatively correlated with O₃ and air temperature. Wind speed was significantly negatively correlated with PM_2.5, and significantly positively correlated with PM₁₀. In terms of pollution transmission, the southwest area of Taiyuan City is a high potential pollution source area of fine particles, and the long-distance transport of PM_2.5 in Xinjiang from the northwest also has a certain contribution to the pollution of fine particles. This study is helpful for us to understand the characteristics and influencing factors of particulate matter pollution in coal production cities.

Machine learning-based estimation of ground-level NO2 concentrations over China

Most current scientific research on NO₂ remote sensing focuses on tropospheric NO₂ column concentrations rather than ground-level NO₂ concentrations; however, ground-level NO₂ concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO₂ concentrations throughout China based on the tropospheric NO₂ column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO₂ and its influencing factors. The R² values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO₂ concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO₂ concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO₂ concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO₂ concentrations indicates that pollution decreased continuously from 2018 to 2020.

The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study

Emissions and meteorology are significant factors affecting aerosol pollution, but it is not sufficient to understand their relative contributions to aerosol pollution changes. In this study, the observational data and the chemical model (GRAPES_CUACE) are combined to estimate the drivers of PM2.5 changes in various regions (the Beijing–Tianjin–Hebei (BTH), the Central China (CC), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)) between the first month after COVID-19 (FMC_2020) (i.e., from 23 January to 23 February 2020) and the corresponding period in 2019 (FMC_2019). The results show that PM2.5 mass concentration increased by 26% (from 61 to 77 µg m−3) in the BTH, while it decreased by 26% (from 94 to 70 µg m−3) in the CC, 29% (from 52 to 37 µg m−3) in the YRD, and 32% (from 34 to 23 µg m−3) in the PRD in FMC_2020 comparing with FMC_2019, respectively. In the BTH, although emissions reductions partly improved PM2.5 pollution (−5%, i.e., PM2.5 mass concentration decreased by 5% due to emissions) in FMC_2020 compared with that of FMC_2019, the total increase in PM2.5 mass concentration was dominated by more unfavorable meteorological conditions (+31%, i.e., PM2.5 mass concentration increased by 31% due to meteorology). In the CC and the YRD, emissions reductions (−33 and −36%) played a dominating role in the total decrease in PM2.5 in FMC_2020, while the changed meteorological conditions partly worsened PM2.5 pollution (+7 and +7%). In the PRD, emissions reductions (−23%) and more favorable meteorological conditions (−9%) led to a total decrease in PM2.5 mass concentration. This study reminds us that the uncertainties of relative contributions of meteorological conditions and emissions on PM2.5 changes in various regions are large, which is conducive to policymaking scientifically in China.

A theory-guided graph networks based PM2.5 forecasting method

The theory-guided air quality model solves the mathematical equations of chemical and physical processes in pollution transportation numerically. While the data-driven model, as another scientific research paradigm with powerful extraction of complex high-level abstractions, has shown unique advantages in the PM_2.5 prediction applications. In this paper, to combine the two advantages of strong interpretability and feature extraction capability, we integrated the partial differential equation of PM_2.5 dispersion with deep learning methods based on the newly proposed DPGN model. We extended its ability to perform long-term multi-step prediction and used advection and diffusion effects as additional constraints for graph neural network training. We used hourly PM_2.5 monitoring data to verify the validity of the proposed model, and the experimental results showed that our model achieved higher prediction accuracy than the baseline models. Besides, our model significantly improved the correct prediction rate of pollution exceedance days. Finally, we used the GNNExplainer model to explore the subgraph structure that is most relevant to the prediction to interpret the results. We found that the hybrid model is more biased in selecting stations with Granger causality when predicting.

Analyzing the spatio-temporal variation of the CO2 emissions from district heating systems with "Coal-to-Gas" transition: Evidence from GTWR model and satellite data in China

Understanding the spatio-temporal heterogeneous effects of socioeconomic and meteorological factors on CO₂ emissions from combinations of different district heating systems with "Coal-to-Gas" transition can contribute to the development of future low-carbon energy systems that are efficient and effective. This work downscales city-level CO₂ emissions to a 3 × 3 km² gridded level in northern China during 2012 to 2018. By employing the Geographically and Temporally Weighted Regression (GTWR) model, nighttime light (NTL) data are adopted as a proxy of the level of urbanization, and the Temperature-Humidity-Wind (THW) Index is used as a proxy of meteorological factors in the downscaling model. The results show that, for more than 85% of the cities, urbanization significantly enhances the CO₂ emissions of district heating systems, while the THW Index shows negative impacts on CO₂ emissions. Significant spatial and temporal heterogeneity exists. The grids with the highest CO₂ emissions from coal-fired boilers (grids with annual variation >0.59 Gg CO₂/year) are mainly located in nonurban areas of the two megacities Beijing and Tianjin and also in the capital cities of each province. Urbanization has larger effects on the CO₂ emissions of natural gas-fired boilers than of coal-fired boilers and combined heat and power (CHP). The average growth rate of CO₂ emissions of gas-fired boilers in the urban areas of the study regions was approximately 4.7 times that of nonurban areas. The spatio-temporal heterogeneous impacts of urbanization on CO₂ emissions should therefore be considered in future discussions of clean heating policies and climate response strategies.

Temporal and Spatial Analysis of PM2.5 and O3 Pollution Characteristics and Transmission in Central Liaoning Urban Agglomeration from 2015 to 2020

The central Liaoning urban agglomeration is an important heavy industry development base in China, and also an important part of the economy in northeast China. The atmospheric environmental problems caused by the development of heavy industry are particularly prominent. Trajectory clustering, potential source contribution (PSCF), and concentration weighted trajectory (CWT) analysis are used to discuss the temporal and spatial pollution characteristics of PM2.5 and ozone concentrations and reveal the regional atmospheric transmission pattern in central Liaoning urban agglomeration from 2015 to 2020. The results show that: (1) PM2.5 in the central Liaoning urban agglomeration showed a decreasing trend from 2015 to 2020. The concentration of PM2.5 is the lowest in 2018. Except for Benxi (34.7 µg/m3), the concentrations of PM2.5 in other cities do not meet the standard in 2020. The ozone concentration in Anshan, Liaoyang, and Shenyang reached the peaks in 2017, which are 68.76 µg/m3, 66.27 µg/m3, and 63.46 µg/m3 respectively. PM2.5 pollution is the highest in winter and the lowest in summer. The daily variation distribution of PM2.5 concentration showed a bimodal pattern. Ozone pollution is the most serious in summer, with the concentration of ozone reaching 131.14 µg/m3 in Shenyang. Fushun is affected by Shenyang intercity pollution, and the ozone concentration is high. (2) In terms of spatial distribution, the high values of PM2.5 are concentrated in monitoring stations in urban areas. On the contrary, the concentration of ozone in suburban stations is higher. The high concentration of ozone in the northeast of Anshan, Liaoyang, Shenyang to Tieling, and Fushun extended in a band distribution. (3) Through cluster analysis, it is found that PM2.5 and ozone in Shenyang are mainly affected by short-distance transport airflow. In winter, the weighted PSCF high-value area of PM2.5 presents as a potential contribution source zone of the northeast trend with wide coverage, in which the contribution value of the weighted CWT in the middle of Heilongjiang is the highest. The main potential source areas of ozone mass concentration in spring and summer are coastal cities and the Bohai Sea and the Yellow Sea. We conclude that the regional transmission of pollutants is an important factor of pollution, so we should pay attention to the supply of industrial sources and marine sources of marine pollution in the surrounding areas of cities, and strengthen the joint prevention and control of air pollution among regions. The research results of this article provide a useful reference for the central Liaoning urban agglomeration to improve air quality.

Exploring the convergence patterns of PM2.5 in Chinese cities

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771-1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

Assessing air pollution in European cities to support a citizen centered approach to air quality management

European cities have made significant progress over the last decades towards clean air. Despite this progress, several cities are still facing acute air pollution episodes, with various urban areas frequently exceeding air quality levels allowed by the European legal standards and WHO guidelines. In this paper, six European cities/ regions (Bristol, UK; Amsterdam, NL; Sosnowiec, PL; Ljubljana, SI; Aveiro, PT; Liguria, IT) are studied in terms of air quality, namely particulate matter, nitrogen dioxide and ozone. The concentrations trends from 2008 to 2017 in the different typology of monitoring stations are addressed, together with the knowledge of daily, weekly and seasonal pollution patterns to better understand the city specific profiles and to characterize pollutant dynamics and variations in multiple locations. Additionally, an analysis of the duration and severity of air pollution episodes is also discussed, followed by an analysis of the fulfillment of the legislated limit values. Each of our 6 case study locations face different air pollution problems, but all these case studies have made some progress in reducing ambient concentrations. In Bristol, there have been strong downward trends in many air pollutants, but the levels of NO₂ remain persistently high and of concern. In recent years, decreasing concentration levels point to some success of Amsterdam air quality policies. PM₁₀ exceedances are a seasonal pollution problem in Ljubljana, Sosnowiec and Aveiro region (even if with different levels of severity). While, exceedances of NO₂ and O₃ concentrations are still problematic in Liguria region. The main findings of this paper are particular relevant to define and compare future citizen-led strategies and policy initiatives that may be implemented to improve and fulfill the EU legislation and the WHO guidelines.

Estimating PM2.5 concentration using the machine learning GA-SVM method to improve the land use regression model in Shaanxi, China

With rapid economic growth, urbanization and industrialization, fine particulate matter with aerodynamic diameters ≤ 2.5 µm (PM_2.5) has become a major pollutant and shows adverse effects on both human health and the atmospheric environment. Many studies on estimating PM_2.5 concentrations have been performed using statistical regression models and satellite remote sensing. However, the accuracy of PM_2.5 concentration estimates is limited by traditional regression models; machine learning methods have high predictive power, but fewer studies have been performed on the complementary advantages of different approaches. This study estimates PM_2.5 concentrations from satellite remote sensing-derived aerosol optical depth (AOD) products, meteorological data, terrain data and other predictors in 2015 in Shaanxi, China, using a combined genetic algorithm-support vector machine (GA-SVM) method, after which the spatial clustering pattern was explored at the season and year levels. The results indicated that temperature (r = -0.684), precipitation (r = -0.602) and normalized difference vegetation index (NDVI) (r = -0.523) were significantly negatively correlated with the PM_2.5 concentration, while AOD (r = 0.337) was significantly positively correlated with the PM_2.5 concentration. Compared to conventional land use regression (LUR) and SVM models and previous related studies, the GA-SVM method demonstrated a significantly better prediction accuracy of PM_2.5 concentration, with a higher 10-fold cross-validation coefficient of determination (R²) of 0.84 and lower root mean square error (RMSE) and mean absolute error (MAE) of 12.1 μg/m³ and 10.07 μg/m³, respectively. Y-scrambling test shows that the models have no chance correlation. The central and southern parts of Shaanxi have high PM_2.5 concentrations, which are mainly due to the pollutant emissions and meteorological and topographical conditions in those areas. There was a positive spatial agglomeration characteristic of regional PM_2.5 pollution, and the spatial spillover effect of PM_2.5 pollution for seasonal and annual variations does exist. In general, the GA-SVM method is robust and accurately estimates PM_2.5 concentrations via a novel modeling framework application and high-quality spatiotemporal information. It also has great significance for the exploration of PM_2.5 pollution estimation and high-precision mapping methods, especially early warning in high-risk areas. Finally, the prevention and control of atmospheric pollution should take pollution control measures from major cities and surrounding cities, and focus on the joint pollution control measures for plain cities.

Spatial Characteristics and Regional Transmission Analysis of PM2.5 Pollution in Northeast China, 2016-2020

Northeast China is an essential industrial development base in China and the regional air quality is severely affected by PM_2.5 pollution. In this paper, spatial autocorrelation, trajectory clustering, hotspot analysis, PSCF and CWT analysis are used to explore the spatial pollution characteristics of PM_2.5 and determine the atmospheric regional transmission pattern for 40 cities in Northeast China from 2016 to 2020. Analysis of PM_2.5 concentration characteristics in the northeast indicates that the annual average value and total exceedance days of PM_2.5 concentration in Northeast China showed a U-shaped change, with the lowest annual average PM_2.5 concentration (31 μg/m³) in 2018, decreasing by 12.1% year-on-year, and the hourly PM_2.5 concentration exploding during the epidemic lockdown period in 2020. A stable PM_2.5 pollution band emerges spatially from the southwest to Northeast China. Spatially, the PM_2.5 in Northeast China has a high degree of autocorrelation and a south-hot-north-cool characteristic, with all hotspots concentrated in the most polluted Liaoning province, which exhibits the H-H cluster pattern and hotspot per year. Analysis of the air mass trajectories, potential source contributions and concentration weight trajectories in Northeast China indicates that more than 74% of the air mass trajectories were transmitted to each other between the three heavily polluted cities, with the highest mean value of PM_2.5 pollution trajectories reaching 222.4 μg/m³, and the contribution of daily average PM_2.5 concentrations exceeding 60 μg/m³ within Northeast China. Pollution of PM_2.5 throughout the Northeast is mainly influenced by short-range intra-regional transport_, with long-range transport between regions also being an essential factor; organized integration is the only fundamental solution to air pollution.

Research on the Temporal and Spatial Characteristics of Air Pollutants in Sichuan Basin

Sichuan Basin is one of the most densely populated areas in China and the world. Human activities have great impact on the air quality. In order to understand the characteristics of overall air pollutants in Sichuan Basin in recent years, we analyzed the concentrations of six air pollutants monitored in 22 cities during the period from January 2015 to December 2020. During the study period, the annual average concentrations of CO, NO2, SO2, PM2.5 and PM10 all showed a clear downward trend, while the ozone concentration was slowly increasing. The spatial patterns of CO and SO2 were similar. High-concentration areas were mainly located in the western plateau of Sichuan Basin, while the concentrations of NO2 and particulate matter were more prominent in the urban agglomerations inside the basin. During the study period, changes of the monthly average concentrations for pollutants (except for O3) conformed to the U-shaped pattern, with the highest in winter and the lowest in summer. In the southern cities of the basin, secondary sources had a higher contribution to the generation of fine particulate matter, while in large cities inside the basin, such as Chengdu and Chongqing, air pollution had a strong correlation with automobile exhaust emissions. The heavy pollution incidents observed in the winter of 2017 were mainly caused by the surrounding plateau terrain with typical stagnant weather conditions. This finding was also supported by the backward trajectory analysis, which showed that the air masses arrived in Chengdu were mainly from the western plateau area of the basin. The results of this study will provide a basis for the government to take measures to improve the air quality in Sichuan Basin.

Exploring the Joint Impacts of Natural and Built Environments on PM2.5 Concentrations and Their Spatial Heterogeneity in the Context of High-Density Chinese Cities

Air pollution in China has attracted wide interest from the public and academic communities. PM2.5 is the primary air pollutant across China. PM2.5 mainly comes from human activities, and the natural environment and urban built environment affect its distribution and diffusion. In contrast to American and European cities, Chinese cities are much denser, and studies on the relationships between urban form and air quality in high-density Chinese cities are still limited. In this paper, we used the ordinary least square (OLS) and geographical weighted regression (GWR) models, selected an already high-density city, Shenzhen, as the study area, and explored the effects of the natural and built environments on air pollution. The results showed that temperature always had a positive influence on PM2.5 and wind speed had a varied impact on PM2.5 within the city. Based on the natural factors analysis, the paper found that an increase in the floor area ratio (FAR) and road density may have caused the increase in the PM2.5 concentration in the central city. In terms of land use mix, land use policies should be adopted separately in the central city and suburban areas. Finally, in terms of spatial heterogeneity, the GWR models achieved much better performances than the global multivariate regression models, with lower AICc and RMSE values and higher adjusted R2 values, ultimately explaining 60% of the variance across different city areas. The results indicated that policies and interventions should be more targeted to improve the air environment and reduce personal exposure according to the spatial geographical context.

Effects of Anthropogenic Emissions from Different Sectors on PM2.5 Concentrations in Chinese Cities

PM_2.5 pollution has gradually attracted people's attention due to its important negative impact on public health in recent years. The influence of anthropogenic emission factors on PM_2.5 concentrations is more complicated, but their relative individual impact on different emission sectors remains unclear. With the aid of the geographic detector model (GeoDetector), this study evaluated the impacts of anthropogenic emissions from different sectors on the PM_2.5 concentrations of major cities in China. The results indicated that the influence of anthropogenic emissions factors with different emission sectors on PM_2.5 concentrations exhibited significant changes at different spatial and temporal scales. Residential emissions were the dominant driver at the national annual scale, and the NO_X of residential emissions explained 20% (q = 0.2) of the PM_2.5 concentrations. In addition, residential emissions played the leading role at the regional annual scale and during most of the seasons in northern China, and ammonia emissions from residents were the dominant factor. Traffic emissions play a leading role in the four seasons for MUYR and EC in southern China, MYR and NC in northern China, and on a national scale. Compared with primary particulate matter, secondary anthropogenic precursors have a more important effect on PM_2.5 concentrations at the national or regional annual scale. The results can help to strengthen our understanding of PM_2.5 pollution, improve PM_2.5 forecasting models, and formulate more precise government control policy.

Analysis of Spatio-Temporal Heterogeneity and Socioeconomic driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

Effect of COVID-19-restrictive measures on ambient particulate matter pollution in Yangon, Myanmar

Background

Particulate matter (PM) is recognized as the most harmful air pollutant to the human health. The Yangon city indeed suffers much from PM-related air pollution. Recent research has interestingly been focused on the novel subject of changes in the air quality associated with the restrictive measures in place during the current coronavirus disease-2019 (COVID-19) pandemic. The first case of COVID-19 in Myanmar was diagnosed on March 23, 2020. In this article, we report on our attempt to evaluate any effects of the COVID-19-restrictive measures on the ambient PM pollution in Yangon.

Methods

We measured the PM concentrations every second for 1 week on four occasions at three study sites with different characteristics; the first occasion was before the start of the COVID-19 pandemic and the remaining three occasions were while the COVID-19-restrictive measures were in place, including Stay-At-Home and Work-From-Home orders. The Pocket PM_2.5 Sensor [PRO] designed by the National Institute for Environmental Studies (NIES), Japan, in cooperation with Yaguchi Electric Co., Ltd., (Miyagi, Japan) was used for the measurement of the ambient PM_2.5 and PM₁₀ concentrations.

Results

The results showed that there was a significant reduction (P < 0.001) in both the PM_2.5 and PM₁₀ concentrations while the COVID-19-restrictive measures were in place as compared to the measured values prior to the pandemic. The city experienced a profound improvement in the PM-related air quality from the “unhealthy” category prior to the onset of the COVID-19 pandemic to the “good” category during the pandemic, when the restrictive measures were in place. The percent changes in the PM concentrations varied among the three study sites, with the highest percent reduction noted in a semi-commercial crowded area (84.8% for PM_2.5; 88.6% for PM₁₀) and the lowest percent reduction noted in a residential quiet area (15.6% for PM_2.5; 12.0% for PM₁₀); the percent reductions also varied among the different occasions during the COVID-19 pandemic that the measurements were made.

Conclusions

We concluded that the restrictive measures which were in effect to combat the COVID-19 pandemic had a positive impact on the ambient PM concentrations. The changes in the PM concentrations are considered to be largely attributable to reduction in anthropogenic emissions as a result of the restrictive measures, although seasonal influences could also have contributed in part. Thus, frequent, once- or twice-weekly Stay-At-Home or Telework campaigns, may be feasible measures to reduce PM-related air pollution. When devising such an action plan, it would be essential to raise the awareness of public about the health risks associated with air pollution and create a social environment in which Telework can be carried out, in order to ensure active compliance by the citizens.

Vertical profile of aerosols in the Himalayas revealed by lidar: New insights into their seasonal/diurnal patterns, sources, and transport

Atmospheric aerosols play a crucial role in climate change, especially in the Himalayas and Tibetan Plateau. Here, we present the seasonal and diurnal characteristics of aerosol vertical profiles measured using a Mie lidar, along with surface black carbon (BC) measurements, at Mt. Qomolangma (QOMS), in the central Himalayas, in 2018-2019. Lidar-retrieved profiles of aerosols showed a distinct seasonal pattern of aerosol loading (aerosol extinction coefficient, AEC), with a maximum in the pre-monsoon (19.8 ± 22.7 Mm^-1 of AEC) and minimum in the summer monsoon (7.0 ± 11.2 Mm^-1 of AEC) seasons. The diurnal variation characteristics of AEC and BC were quite different in the non-monsoon seasons with enriched aerosols being maintained from 00:00 to 10:00 in the pre-monsoon season. The major aerosol types at QOMS were identified as background, pollution, and dust aerosols, especially during the pre-monsoon season. The occurrence of pollution events influenced the vertical distribution, seasonal/diurnal patterns, and types of aerosols. Source contribution of BC based on the weather research and forecasting chemical model showed that approximately 64.2% ± 17.0% of BC at the QOMS originated from India and Nepal in South Asia during the non-monsoon seasons, whereas approximately 47.7% was from local emission sources in monsoon season. In particular, the high abundance of BC at the QOMS in the pre-monsoon season was attributed to biomass burning, whereas anthropogenic emissions were the likely sources during the other seasons. The maximum aerosol concentration appeared in the near-surface layer (approximately 4.3 km ASL), and high concentrations of transported aerosols were mainly found at 4.98, 4.58, 4.74, and 4.88 km ASL in the pre-monsoon, monsoon, post-monsoon, and winter seasons, respectively. The investigation of the vertical profiles of aerosols at the QOMS can help verify the representation of aerosols in the air quality model and satellite products and regulate the anthropogenic disturbance over the Tibetan Plateau.

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

Estimating the impact of ground ozone concentrations on crop yields across China from 2014 to 2018: A multi-model comparison

Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O₃-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.4%-49.3% and the total RYL of rice was 6.2%-52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9-130.4 and 28.3-35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5-44.7 and 11.0-15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O₃-crop models when applying to large scales. Instead of applying one unified O₃-crop models to all regions across China, we also explored the strategy of employing specific O₃-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O₃-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O₃-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O₃-crop interactions across regions, a systematic framework with massive regional O₃-crop models should be properly designed and implemented.

Coastal meteorology on the dispersion of air particles at the Bachok GAW Station

Meteorology over coastal region is a driving factor to the concentration of air particles and reactive gases. This study aims to conduct a research to determine the level of year-round air particles and the interaction of the meteorological driving factors with the particle number and mass in 2018, which is moderately influenced by Southeast Asian haze. We obtained the measurement data for particle number count (PNC), mass, reactive gases, and meteorological factors from a Global Atmospheric Watch (GAW) station located at Bachok Marine Research Center, Bachok, Kelantan, Malaysia. For various timeseries and correlation analyses, a 60-second resolution of the data has been averaged hourly and daily and visualized further. Our results showed the slight difference in particle behavior that is either measured by unit mass or number count at the study area. Diurnal variations showed that particles were generally high during morning and night periods. Spike was observed in August for PM_2.5/PNC_2.5 and PM₁₀/PNC₁₀ and in November for PM_Coarse/PNC_Coarse. From a polar plot, the particles came from two distinct sources (e.g., seaside and roadside) at the local scale. Regional wind vector shows two distinct wind-blown directions from northeast and southwest. The air mases were transported from northeast (e.g., Philippines, mainland China, and Taiwan) or southwest (e.g., Sumatra) region. Correlation analysis shows that relative humidity, wind direction, and pressure influence the increase in particles, whereas negative correlation with temperature is observed, and wind speed may have a potential role on the decline of particle concentration. The particles at the study area was highly influenced by the changes in regional wind direction and speed.