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

Both meteorological changes and emission control have significant impacts on PM 2.5 concentration and regional transport contribution, where it is important to clarify the relationship among them for air pollution control. In this paper, the WRF-CAMx coupled with a set of simulation scenarios was employed for decomposing the contributions of meteorological conditions, emission, and regional transport considering transport matrix to PM 2.5 concentration in the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions during 2013–2017. The results show that the local contributions occupied the primary position in PM 2.5 concentrations in the BTH and YRD regions, reaching 57.9% and 58.1% in 2017, respectively. In terms of regional transports, they were mainly from Shandong Peninsula (SP) and Fen Nutrient Plain (FNP) for BTH, while Central triangle Region (CTR) and SP for YRD. Analysis of typical representative months suggested that in January, the concentration of local contribution reached peak. In addition, the results also show that when the intensity of emission reduction increased, the proportion of local contribution increased significantly while the local absolute contribution concentration decreased correspondingly. The meteorological conditions had little impact on transport matrix dominated by local contribution, whereas, the more favorable the meteorological diffusion conditions, the lower the contribution ratio of the out-of-region transport and the lower the contribution concentration. Moreover, the variation of meteorological conditions was more significant to the transport contribution outside the region. These findings can be helpful for formulating reasonable strategies of regional joint prevention and control and providing a valuable preference for other areas of China. [Display omitted] • The transport matrix of PM 2.5 was established using the WRF-CAMx model. • Emission reduction dominated the total decrease in PM 2.5. • Local emissions contributed 57.9% and 58.1% of PM 2.5 in BTH and YRD. • Meteorological conditions had a significant impact on the regional transport. [ABSTRACT FROM AUTHOR]

The PM 2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM 2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM 2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM 2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM 2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM 2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings. [ABSTRACT FROM AUTHOR]

The recently released Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 introduced a fine scale aerosol optical depth (AOD) distribution, the 3 km product, which is expected to perform well in analyzing aerosols and identifying local air pollution, especially in the severely polluted atmosphere of China. However, few detailed evaluations of regional variations have been conducted. In this paper, we evaluate MODIS 3 km and 10 km AOD products for China against ground-based measurements and compare their performance with respect to spatial and temporal variations. The ground validations indicate that the two products are generally correlated well to ground-based observations. Spatially, the 3 km product slightly outperform the 10 km product in well-developed areas of southern China. Temporally, both products perform worse during spring and summer. Atmospheric clouds and underlying surface are two key factors that influence the accuracy and number of retrievals for both products. The comparison analysis reveals the newly introduced AOD product clearly shows good relationships with the coarse resolution retrievals in spatial and temporal variation but significant differences regarding details. The 3 km AOD product provides better aerosol gradients, more retrievals in bare areas of western China and some spikes of diurnal variation in cloudy days. Seasonal comparisons show the 3 km AOD product is higher than the 10 km product in all seasons, especially during spring and summer. Although the 3 km product for China generally performs slightly worse than the 10 km product, the added information of the MODIS 3 km AOD product shows potential for studying local aerosol characterization, and may facilitate studies of air pollution. [ABSTRACT FROM AUTHOR]

Vehicle emissions have become a major source of air pollution in Shandong province, which has experienced a sharp growth of vehicle numbers in recent years and now has the largest vehicle population in China. This paper combines the COPERT IV model with the vehicle age distribution to estimate the temporal trends and map the spatial distributions of vehicle emissions in Shandong province during the period ranging from 2000 to 2014. Both conventional air pollutants and greenhouse gases are included. In addition, a high-resolution vehicle emission inventory at the prefecture level is developed and mapped on a 0.05° × 0.05° grid based on road information. Our results show that the emissions of all of the conventional air pollutants have decreased to various extents over the recent past, but greenhouse gas emissions have continued to increase due to the lack of effective control strategies. The total emissions of CO, NMVOC, NO X , PM 10 , CO 2 , CH 4 and N 2 O from the Shandong vehicle fleet changed from 1734.5 Gg, 277.9 Gg, 177.0 Gg, 12.4 Gg, 19239.7 Gg, 11.3 Gg and 0.6 Gg, respectively, in 2000 to 1723.3 Gg, 234.2 Gg, 513.8 Gg, 29.5 Gg, 138,419.5 Gg, 15.3 Gg and 3.9 Gg, respectively, in 2014. Vehicle emissions were mainly concentrated in cities and became more dispersed in Shandong province between 2000 and 2014. [ABSTRACT FROM AUTHOR]

Particulate air pollution has had a significant impact on human health in China and it is associated with cardiovascular and respiratory diseases and high mortality and morbidity. These health impacts could be translated to reduced labor availability and time. This paper utilized a supply-driven input-output (I-O) model to estimate the monetary value of total output losses resulting from reduced working time caused by diseases related to air pollution across 30 Chinese provinces in 2007. Fine particulate matter (PM 2.5 ) pollution was used as an indicator to assess impacts to health caused by air pollution. The developed I-O model is able to capture both direct economic costs and indirect cascading effects throughout inter-regional production supply chains and the indirect effects greatly outnumber the direct effects in most Chinese provinces. Our results show the total economic losses of 346.26 billion Yuan (approximately 1.1% of the national GDP) based on the number of affected Chinese employees (72 million out of a total labor population of 712 million) whose work time in years was reduced because of mortality, hospital admissions and outpatient visits due to diseases resulting from PM 2.5 air pollution in 2007. The loss is almost the annual GDP of Vietnam in 2010. The proposed modelling approach provides an alternative method for health-cost measurement with additional insights on inter-industrial and inter-regional linkages along production supply chains. [ABSTRACT FROM AUTHOR]

In this paper, the spatial dynamics of the atmospheric particulate matters (resp. PM 10 and PM 2.5 ) are studied using turbulence methodologies. It is found experimentally that the spatial correlation function ρ ( r ) shows a log-law on the mesoscale range, i.e., 50 ≤ r ≤ 500 km, with an experimental scaling exponent β = 0.45. The spatial structure function shows a power-law behavior on the mesoscale range 90 ≤ r ≤ 500 km. The experimental scaling exponent ζ ( q ) is convex, showing that the intermittent correction is relevant in characterizing the spatial dynamic of particulate matter. The measured singularity spectrum f ( α ) also shows its multifractal nature. Experimentally, the particulate matter is more intermittent than the passive scalar, which could be partially due to the mesoscale movements of the atmosphere, and also due to local sources, such as local industry activities. [ABSTRACT FROM AUTHOR]

Recently, the PM 2.5 pollution in China has occurred frequently and caused widely concern. In order to identify the key factors for PM 2.5 generation, the formation characteristics of PM 2.5 would be revealed. A property of electric neutrality of PM 2.5 was proposed under the least-energy principle and verified through electricity-charge calculation in this paper. It indicated that PM 2.5 is formed by the effect of electromagnetic force, including the effect of ionic bond, hydrogen bond and polarization. According to the analysis of interactive forces among different chemical components, a simulation model is developed for describing the random process of PM 2.5 generation. In addition, an orthogonal test with two levels and four factors has been designed and carried out through the proposed model. From the text analysis, PM 2.5 would be looser and suspend longer in atmosphere due to Organic Compound ( OC ) existing ( OC can reduce about 67% of PM 2.5 density). Considering that N H 4 + is the only cation in the main chemical components of PM 2.5 , it would be vital for anions (such as S O 4 2 − and N O 3 − ) to aggregate together for facilitating PM 2.5 growing. Therefore, in order to relieve PM 2.5 pollution, control strategies for OC and N H 4 + would be enhanced by government through improving the quality of oils and solvent products, decreasing the amount of nitrogenous fertilizer utilization, or changing the fertilizing environment from dry condition to wet condition. [ABSTRACT FROM AUTHOR]

A thermal internal boundary layer (TIBL) can cause the fumigation effect and reduce the dispersion capacity of air pollutants. The paper's goal is to quantify the interactions of pollutants between cities subjected to enhanced fumigation effects. To that end, an observational campaign was launched at the Shandong Peninsula of China in the winter of 2020. The campaign focused on monitoring vertical profiles of aerosols, wind field, temperature, and humidity for the boundary-layer dynamic-thermodynamic structure. The Nested Air Quality Prediction Modeling System (NAQPMS) coupled with online source-tagging model was also employed to delve into the source-receptor relationships of the cities involved in the fumigation effect. The results indicated that the presence of a TIBL structure triggered the prolonged and widespread pollution episodes. This resulted in a temperature inversion (250–1000 m in altitude) near the convergence line on surface and rendered the atmospheric boundary layer more stably stratified, i.e., a situation that was unfavorable for the diffusion of pollutants against a persistently sinking flow (with a mean velocity of roughly 0.2 m s−1) for a prolonged time. Meanwhile, TIBLs prevented the fumigation areas from interacting with adjacent inland areas (Shandong Peninsula's local contribution accounting for >90% of the PM 2.5) and facilitated inter-city transport of pollutants inside this peninsula (the transport contribution between cities reached up to 55–75%). Such distinct behaviors as observed over this coastal area render air pollution control more of an elusive issue, as is referenced to several inland cities. [Display omitted] ● Quantifying the interactions of pollutants between cities subjected to enhanced fumigation effects ● The presence of a TIBL structure triggered the prolonged and widespread pollution episodes ● TIBLs made the atmospheric boundary layer more stably stratified and unfavorable diffusion of pollutants with strong sinking flowing ● TIBLs prevented fumigation areas from interacting with adjacent inland areas and facilitated inter-city transport of pollutants inside it [ABSTRACT FROM AUTHOR]

The role of precursors' concentrations and meteorological conditions on the emerging ozone pollution problem in China has received wide attention, especially after the releasing of the Air Pollution Prevention Control and Action Plan (APPC) since 2013. With the decreasing trend of PM 2.5 nationwide, the effect of the strict control measures on increasing ozone variation has less been studied due to the challenge of complexity of nonlinear relationship among a number of factors on ozone formation. This paper evaluated the influence of both ozone precursors and meteorology on maximum daily average 8 h (MDA8) ozone at two urban sites and one rural site in Hebei province, China, from 2013 to 2017, by using a combined application of Kolmogorov-Zurbenko (KZ) filter and artificial neural network (ANN) model. Results showed that R2 was 0.80 between the measured and the simulated MDA8 ozone concentration when using meteorological factors and precursors' concentrations as input variables for ANN model. However, ANN model has limitation in estimating O 3 concentration peaks. The values of threat score (TS), probability of detection (POD) and false alarm rate (FAR) for MDA8 ozone concentration were 39%, 44% and 21%, respectively, throughout three studied sites in Hebei province. The annual average percentage change of precursor-related ozone was 0.67% from 2014 to 2017. Temperature, atmospheric pressure and boundary layer height were shown to account for 64% of the variability in long-term ozone levels. Ozone variation in Hebei province was reproduced mainly by meteorological parameters, and the contribution from precursors' concentration was smaller during the years when the APPC was implemented. [Display omitted] • Ozone level was estimated based on precursors' level and meteorological factors. • The ambient ozone concentration estimated by ANN model was acceptable. • Meteorology dominates the ozone variation in 2013–2017 in Hebei province. [ABSTRACT FROM AUTHOR]

Dust aerosols from the desert areas of China can be lifted to the upper troposphere and transported eastward by the westerly, affecting regional and local air pollution process. In this paper, we analyzed the long-range transport of dust and its impact on the haze-fog process that occurred in Tianjin from 23 November to 5 December 2018. Satellite and ERA5 reanalysis data revealed that dust from northwestern China was transported to eastern China by the westerly flow behind the troughs, which subsequently enhanced the air pollution in Tianjin area. On 26 November and 2 December, the visibility was less than 2 km with a relative humidity (RH) over 90%, indicating there was fog weather in Tianjin. The dust influx from the northwest direction mixed with ground pollutants, increased the PM 2.5 and PM 10 mass concentrations and enhanced the air pollution with the cumulative peak value of the PM 2.5 mass concentration reaching over 300 μg/m3 on 26 November. On 27 November and 3 December, the wind speed increased and large dust particles were transported to Tianjin, the PM 10 mass concentration exceeded a much higher value of 550 μg/m3 whereas the PM 2.5 mass concentration decreased to its lowest value, indicating dust weather followed the typical haze-fog pollution period in Tianjin. The ground lidar observations indicated that a large amount of non-spherical dust aerosols transmitted to Tianjin at 1.5~2.5 km height and settled down near the ground, which substantially affected the aerosol optical characteristics and air quality over the area. The influx of dust successive after the haze-fog weather, aggravated the air pollution process in Tianjin area, make it lasting for 153 h. • Long-range transport of dust and its effects on air pollution in Tianjin were studied. • The process of air pollution includes haze, fog and dust weather. • Dust alternates with haze-fog, make the duration of pollution lasting for 153 h. [ABSTRACT FROM AUTHOR]

The air pollution in China has presented remarkable regional and complex characteristics, and particle explosive growth events happened frequently. In view of the domestic situation, a new synergistic approach, which could detect particle pollution over large area rapidly, is urgently needed. A rapid detection approach based on multiple mobile vehicle-based lidar systems is proposed to meet the needs of regional particle pollution observation in this paper. Take the Jing-Jin-Ji (the abbreviation of Beijing-Tianjin-Hebei) area for example, the methodology and practice of the rapid synergistic approaches with three sets of mobile vehicle-based lidars are presented. On the premise of synergistic observation scheme design and data quality control, we conducted a synergistic observation of aerosol in Jing-Jin-Ji area on 4 November 2017. Then the spatial distribution and transport characteristics of particle pollution in this area are discussed. The synergistic observation results show that high aerosol extinction was observed on the southwest pathway in this case. Furthermore, the spatial distribution characteristics of aerosol optical depth and boundary layer height were analyzed in this study. The comprehensive analysis of synergistic observation results, backward trajectories, and near-surface concentration of PM 2.5 based on in situ instrument further illustrated the transport from the southwest pathway strongly affects air quality. The synergistic approach based on multiple mobile vehicle-based lidars provides an effective approach on regional pollution tracking and characterizing within hours. • A synergistic approach for regional pollution tracking based on multiple mobile vehicle-based lidars was presented. • Characteristics of the regional distribution of aerosol particles in Jing-Jin-Ji were observed. • The significant AOD and BLH was found together on the southwest pathway in the early stage of haze development. [ABSTRACT FROM AUTHOR]

In recent years, near-surface ozone (O 3) concentrations have been increasing, which aggravates O 3 pollution. Due to the environmental threat it poses to human health, O 3 pollution has become a hot topic among researchers. In this paper, we used a generalized additive model (GAM) to evaluate the complex nonlinear relationships between O 3 concentration and the factors influencing O 3 concentration from 2013 to 2017 in Lanzhou in Western China. We considered factors such as long-term trend, seasonality at a quarterly interval, and the weekend effect. The results showed that O 3 concentration in Lanzhou was affected by many factors, and these influencing factors were correlated with each other. In the single-factor model, the adjusted coefficient of determination (R2) was 0.594, and the total deviance explained of O 3 concentration was 62.3%. We detected significantly nonlinear relationships between O 3 concentration and the influencing factors like air temperature, sunshine hours, wind speed, relative humidity, and the concentrations of NO 2 and PM 2.5. In particular, air temperature was the main driving factor for O 3 concentration, which explained 24.2% of the variance (F = 350.84). In addition, we built a double-factor model to investigate the interactive influences of these influencing factors on O 3 concentration variation. In the fitted double-factor model, R2 was 0.636 and the total deviance explained was 68.1%, both of which were higher (i.e., better performance) than that in the single-factor model. Among these studied influencing factors, the interaction between air temperature and air pollutants showed the greatest influence on O 3 concentration variation. The results of this study can be used to assist local environmental authorities to take proactive measures for O 3 pollution control in Lanzhou, Gansu. • The ozone concentration in Lanzhou is significantly correlated with meteorological elements and other pollutants. • Temperature is the main driving factor for the change of ozone concentration in Lanzhou. • The interaction between temperature and other influencing factors has a significant impact on ozone concentration. • The generalized additive model (GAM) with interaction term can explain the change of ozone concentration more reasonably. [ABSTRACT FROM AUTHOR]