A new scheme of PM2.5 and O3 control strategies with the integration of SOM, GA and WRF-CAMx.

Previous air pollution control strategies didn't pay enough attention to regional collaboration and the spatial response sensitivities, resulting in limited control effects in China. This study proposed an effective PM2.5 and O3 control strategy scheme with the integration of Self-Organizing Map (SOM), Genetic Algorithm (GA) and WRF-CAMx, emphasizing regional collaborative control and the strengthening of control in sensitive areas. This scheme embodies the idea of hierarchical management and spatial-temporally differentiated management, with SOM identifying the collaborative subregions, GA providing the optimized subregion-level priority of precursor emission reductions, and WRF-CAMx providing response sensitivities for grid-level priority of precursor emission reductions. With Beijing-Tianjin-Hebei and the surrounding area (BTHSA, "2 + 26" cities) as the case study area, the optimized strategy required that regions along Taihang Mountains strengthen the emission reductions of all precursors in PM2.5-dominant seasons, and strengthen VOCs reductions but moderate NOx reductions in O3-dominant season. The spatiotemporally differentiated control strategy, without additional emission reduction burdens than the 14th Five-Year Plan proposed, reduced the average annual PM2.5 and MDA8 O3 concentrations in 28 cities by 3.2%-8.2% and 3.9%-9.7% respectively in comparison with non-differential control strategies, with the most prominent optimization effects occurring in the heavily polluted seasons (6.9%-18.0% for PM2.5 and 3.3%-14.2% for MDA8 O3, respectively). This study proposed an effective scheme for the collaborative control of PM2.5 and O3 in BTHSA, and shows important methodological implications for other regions suffering from similar air quality problems.

Influence of urbanization on meteorological conditions and ozone pollution in the Central Plains Urban Agglomeration, China.

Changes in aerodynamic and thermal conditions caused by urbanization can impact regional meteorological conditions, subsequently affecting air quality. Updated Moderate-resolution Imaging Spectroradiometer (MODIS) land use data and coupled with the urban canopy models (UCMs) in the Weather Research and Forecasting (WRF) model. This enabled the impact of urban land expansion on meteorological conditions and ozone (O3) concentrations to be evaluated. Urban expansion increased the temperature at 2 m (T2) and the probability of precipitation in urban expansion areas, and enhanced the surface urban heat island at night. As the expansion areas became progressively larger, the increase in T2 became more pronounced. The proportions of urban surfaces in June 2016, 2018, and 2020 compared to 2001 increased by 0.69%, 0.83%, and 1.04%, respectively, while T2 increased by 0.12, 0.19, and 0.20 °C in urban areas, respectively. With urban expansion, the O3 concentration increased by 1.12, 1.37, and 0.76 µg/m3 (three-year averages) in urban, suburban, and rural areas, respectively. After coupling a multi-layer urban canopy model (building effect parameterization, BEP), or a multi-layer urban canopy model with a building energy model including anthropogenic heat due to air conditioning (BEP + BEM, abbreviated as BEM simulation), the O3 concentration changed significantly in the urban expansion area, compared to the results of a single-layer urban canopy model (UCM). O3 concentrations decreased most in the BEP simulation (-0.77 µg/m3), while O3 concentrations increased most in the BEM simulation (+1.85 µg/m3). The average observed O3 concentration was 108.35 µg/m3 (three-year average), while the simulated value was 75.65-83.72 µg/m3 (R = 0.69-0.77). The validation results in the BEM and Global Optimal Scenario (GOS) simulations were relatively good, with the GOS simulation producing slightly better results than the BEM. The simulation of O3 in urban agglomerations could be improved by integrating the results of the UCMs.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model.

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Development of an integrated machine-learning and data assimilation framework for NOx emission inversion.

As major air pollutants, nitrogen oxides (NOx, mainly comprising NO and NO2) not only have adverse effects on human health but also contribute to the formation of secondary pollutants, such as ozone and particulate nitrate. To acquire reasonable NOx simulation results for further analysis, a reasonable emission inventory is needed for three-dimensional chemical transport models (3D-CTMs). In this study, a comprehensive emission adjustment framework for NOx emission, which integrates the simulation results of the 3D-CTM, surface NO2 measurements, the three-dimensional variational data assimilation method, and an ensemble back propagation neural network, was proposed and applied to correct NOx emissions over China for the summers of 2015 and 2020. Compared with the simulation using prior NOx emissions, the root-mean-square error, normalized mean error, and normalized mean bias decreased by approximately 40 %, 40 %, and 60 % in NO2 simulation using posterior NOx emissions corrected by the framework proposed in this work. Compared with the emissions for 2015, the NOx emission generally decreased by an average of 5 % in the simulation domain for 2020, especially in Henan and Anhui provinces, where the percentage reductions reached 24 % and 19 %, respectively. The proposed framework is sufficiently flexible to correct emissions in other periods and regions. The framework can provide reliable and up-to-date emission information and can thus contribute to both scientific research and policy development relating to NOx pollution.

Understanding the underlying mechanisms governing the linkage between atmospheric oxidative capacity and ozone precursor sensitivity in the Yangtze River Delta, China: A multi-tool ensemble analysis.

Continued exacerbation of ozone (O3) pollution in China has driven the urgent need for an emission control strategy that efficiently reduces O3 levels. Determining O3 precursor sensitivity (OPS) and its driving factors is a prerequisite for formulating effective O3 control strategies. In this study, we proposed an atmospheric oxidative capacity-based indicator, HO2/OH, and demonstrated its effectiveness in indicating OPS over the Yangtze River Delta (YRD) of China by applying a localized comprehensive air quality model with extensions (CAMx) coupled with the Weather Research and Forecasting (WRF) model. A strong correlation was discovered between HO2/OH and OPS, and HO2/OH showed the best performance in separating NOx- and VOC-limited regimes in comparison with other commonly used indicators. A comprehensive analysis with ensemble diagnostic tools revealed the spatial heterogeneity of NOx and VOC emissions and the impact of regional transport controlling the relationship between OPS variations and the HO2/OH indicator over the YRD. The process analysis results show that days with higher contributions from horizontal advection favored OPS transitions in Shanghai, Nanjing, Hefei, Suzhou, and Wuhu, while vertical advection caused OPS transitions in Hangzhou and Ningbo. O3 source apportionment technology analysis indicated that the regional contributions from Zhejiang and Jiangsu/Anhui corresponded well to the NOx-limited and VOC-limited regimes, respectively. Our results provide a better understanding of the underlying mechanisms of the relationship between OPS and the HO2/OH indicator and can help guide contingency control measures for O3 despiking over the YRD and other photochemically active regions worldwide.

Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region.

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study's comparison of the three emission inventories' data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.

Development and application of a hybrid long-short term memory - three dimensional variational technique for the improvement of PM2.5 forecasting.

The current state-of-the-art three-dimensional (3D) numerical model for air quality forecasting is restricted by the uncertainty from the emission inventory, physical/chemical parameterization, and meteorological prediction. Forecasting performance can be improved by using the 3D-variational (3D-VAR) technique for assimilating the observation data, which corrects the initial concentration field. However, errors from the prognostic model cause the correction effects at the first hour to be erased, and the bias of the forecast increases relatively fast as the simulation progresses. As an emerging alternative technique, long short-term memory (LSTM) shows promising performance in air quality forecasting for individual stations and outperforms the traditional persistent statistical models. In this study, a new method was developed to combine a 3D numerical model with 3D-VAR and LSTM techniques. This method integrates the advantage of LSTM, namely its high-accuracy forecasting for a single station and that of the 3D-VAR technique, namely its ability to extend improvement to the whole simulation domain. This hybrid method can effectively improve PM2.5 forecasting for the next 24 h, relative to forecasting with the 3D-VAR technique which uses the initial hour concentration correction. Results showed that the root-mean-square error and normalized mean error were decreased by 29.3% and 33.3% in the validation stations, respectively. The LSTM-3D-VAR method developed in this study can be further applied in other regions to improve the forecasting of PM2.5 and other ambient pollutants.

[Air Pollution Characteristics and Quantitative Evaluation of Multi-scale Transport in the Beijing-Tianjin-Hebei Region in January, 2016].

Based on atmospheric monitoring data and the WRF-CAMx model, this study analyzed the characteristics of air pollution and performed a quantitative assessment of PM2.5 cross-border transport in the Beijing-Tianjin-Hebei (BTH) region in January 2016. The results showed that the average concentrations of PM2.5, PM10, SO2, NO2, and CO were 89.5 µg·m-3, 135.61µg·m-3, 57.55µg·m-3, 60.79µg·m-3, and 2.12 mg·m-3, respectively, indicating severe PM2.5 pollution. During the study period, surface-level PM2.5 in each city of BTH region was dominated by local emissions, which accounted for 45.4% to 69.9%. The regional transport contribution was supplemented by transport from within and outside of the BTH region, accounting for 4.8% to 49.7% and 4.9% to 29.6%, respectively. In addition, high wind speeds promoted the diffusion of local PM2.5 pollution and cities with high upwind pollution enhance regional-scale transport to downwind cities. The total inflow, outflow, and net flux of PM2.5 in Beijing (Shijiazhuang) in January 2016 were 1582.96 t·d-1 (2036.89 t·d-1), -1171.09 t·d-1 (-1879.12 t·d-1), and 411.87 t·d-1 (157.77 t·d-1), respectively, indicating that PM2.5 inputs from surrounding cities per unit time were higher than external inputs to the surrounding cities. Furthermore, net PM2.5 flux showed notable vertical evolution; the total net flux of PM2.5 in Beijing and Shijiazhuang below 1782 m ranged from 17.86 to 64.18 t·d-1 and -2.95 to 134.81 t·d-1, respectively, and both peaked 817 m above the ground at 64.18 and 134.81 t·d-1. Moreover, a significant increase the net PM2.5 inflow flux in Zhangjiakou and Shanxi explained the observed net flux peaks in these two cities.

[Comparison Analysis of the Effect of Emission Reduction Measures for Major Events and Heavy Air Pollution in the Capital].

Taking the "9.3" military parade in 2015 and two red alerts of heavy air pollution in December of the same year as examples, the characteristics of meteorological factors and pollutant concentration variation were compared. Based on the estimation of pollutant emission reduction under different periods, the WRF-CAMx model was used to evaluate the effect of PM2.5 pollution improvement. The results showed that the daily average PM2.5 concentration (19.0 µg·m-3) during the parade (from August 20 to September 4) decreased by 60.0% and 48.0%, respectively, in comparison to that before (August 15-19) and after (September 5-15) the parade. The daily average PM2.5 concentration (232.3 µg·m-3) during the first red alert period was higher than that of the second red alert (216.6 µg·m-3). The air quality before the second red alert was better than that before the first red alert. The proportion of emission reduction during the parade was generally larger than that during the red alert periods, which provided a controllable and favorable condition for the realization of the "Parade Blue". The PM2.5 concentration in Beijing decreased by 32.4%, 17.1%, and 22.0% under the control measures during the periods of the "9.3" parade, the first red alert, and second red alert, respectively. The higher proportion of PM2.5 concentration reduction could be attributed to the more intensive regional emission reduction and the favorable meteorological conditions. The intensity of the pollution reduction, the timing of the implementation of emergency control measures, and meteorological conditions were the most important factors that may have influenced the improvement of pollution.

[Concentration Characteristics of PM2.5 and the Causes of Heavy Air Pollution Events in Beijing During Autumn and Winter].

To study the changing of characteristics and formation mechanisms of PM2.5 in Beijing during the last two years, particulate matter concentrations, weather conditions, and air-mass trajectories were analyzed during severe pollution episodes in fall and winter 2016-2017 using routine observations and the TrajStat model. Results showed that 13 heavy pollution events, each lasting at least two days, occurred in Beijing. Of these, approximately 61.5% occurred in winter, characterized by heavier pollution concentrations and longer durations than those occurring in autumn. A low-pressure gradient, high humidity, low surface wind speed, low boundary layer, and particular terrain (i. e., being surrounded by mountains on three sides) all contributed to the high occurrence frequency of severe pollution episodes in autumn and winter. During the pollution episodes, the average ratio of PM2.5 to PM10 reached 0.86. The air-masses during the accumulation stage were mainly transported from the northwest, west, southwest, and southeast of Beijing. The southwestern and southeastern transmission paths accounted for 21.6% of the total pollution load. In addition, the WRF-CAMx model was used to quantitatively analyze the contributions of local and external sources to the concentrations of PM2.5 in Beijing during 16-22 December 2016. Based on this analysis, PM2.5 contributions notably varied with different air-masses; in the case of southern air-masses, external sources dominated the PM2.5 concentrations in Beijing and local contributions decreased rapidly; in contrast, in the case of northwestern air-masses, the opposite pattern occurred. Overall, the contribution of local sources to PM2.5 concentrations in Beijing varied from 16.5% to 69.3% during the monitored pollution episodes.

Differences in concentration and source apportionment of PM2.5 between 2006 and 2015 over the PRD region in southern China.

During China's 11th Five Year Plan (FYP) and 12th FYP (2006-2015), a series of air pollution control measures was implemented in the Pearl River Delta (PRD) region. Therefore, it is vital to determine how the concentration and sources of fine particulate matter (PM2.5) in this region changed between 2006 and 2015. In this work, using 2006 and 2015 emission inventories, the concentration and source apportionment of PM2.5 were simulated using the Weather Research and Forecast - Comprehensive Air Quality Model with Extensions (WRF-CAMx) for January, April, July and October in the PRD region. The PM2.5 in 10 cities and the contributions made by sources in six major categories were tracked using the Particulate Source Apportionment Technology (PSAT) module. The results showed that the PM2.5 concentration was lower across the entire PRD region in the 2015 emission scenario than in the 2006 scenario, and that the degree of this reduction exceeded 40 µg/m3 in some places. The PM2.5 contributed by mobile emissions decreased the most, especially in Guangzhou, Foshan and Shenzhen, where mobile contributions decreased from 15.0, 17.9 and 13.0 µg/m3 in 2006 to 2.6, 3.1 and 4.1 µg/m3 in 2015, respectively. The PM2.5 contributed by power plants also decreased, and in Dongguan and Guangzhou, the extent of this reduction reached 2.5 and 3.4 µg/m3 respectively. However, due to an increase in industrial production and population size, the PM2.5 from industrial point sources and area sources also increased between 2006 and 2015 in some of the cities. Investigation of the source apportionment for city centers yielded similar results. In addition to emissions within the PRD region, outside-PRD non-local contribution is still an important PM2.5 contributor. Hence, more stringent policies for controlling industrial and area sources and deepening province-to-province cooperation are urgently needed as the next step in PM2.5 control.

[Pollution Characteristics and Regional Transport of Atmospheric Particulate Matter in Beijing from October to November, 2016].

In this study, the Aerosol Chemical Speciation Monitor (ACSM) was used to conduct real-time and continuous comprehensive observation of chemical components in non-refractory submicron aerosols (NR-PM1) from October 15 to November 15, 2016. In addition to that, the evolution characteristics of NR-PM1 chemical components were discussed. The potential source contribution function (PSCF) method and a meteorology-air quality coupling model system (WRF-CAMx) were applied to identify the potential PM2.5 emission sources and transport path in Beijing, and the vertical distribution characteristics of PM2.5 net transport flux. The results indicate that the monthly average mass concentrations of NR-PM1 and PM2.5 were (59.16±57.05) µg·m-3 and (89.82±66.66) µg·m-3, respectively. On average, NR-PM1 accounted for (70.31±22.28)% of PM2.5. During the whole study period, Org, NO3-, SO42-, NH4+, and Chl represented (42.75±11.35)%, (21.27±7.72)%, (19.11±7.08)%, (12.19±2.64)%, and (4.68±3.24)% of NR-PM1, respectively. The diurnal variation characteristics of different chemical components were disparate. The high potential source areas were mainly located in southern Hebei, northeastern Henan, and western Shandong provinces during the whole study period. During the haze episode, the potential regions of higher contribution were concentrated in Baoding, southern Beijing, and Langfang. The simulation results of WRF-CAMx showed that the vertical distribution characteristics of PM2.5 net flux varied with different altitudes. The adjacent cities mainly export PM2.5 to Beijing, and the PM2.5 net fluxes mainly occurred at 600-800 m during the whole study period. PM2.5 in Beijing from external sources mainly occurred in high altitudes during the early stage of the heavy pollution episode. Then it turned to near-ground transport until November 5, when the pollution was the most severe. This indicated that high-altitude and near-ground transport both played an essential role in the formation of heavy PM2.5 pollution in Beijing during the autumn. Moreover, two important transport pathways were identified:the southwest-northeast pathway (Baoding→Beijing→Chengde) and the northwest-southeast pathway (Zhangjiakou→Beijing→Langfang-south→Tianjin).

[Air Pollutant Emission Inventory from Iron and Steel Industry in the Beijing-Tianjin-Hebei Region and Its Impact on PM2.5].

The iron and steel industry, which discharges a large amount of pollutants including SO2, NOx, and PM2.5, is the main source of atmospheric pollution in the Beijing-Tianjin-Hebei region. Based on the bottom-up method, a high temporal and spatial resolution emission inventory of the iron and steel industry in the Beijing-Tianjin-Hebei region was developed, which took into account the multiple air pollutants released during coking, sintering, pelletizing, ironmaking, steelmaking, and the steel rolling process. As the emission inventory showed, the total emissions of SO2, NOx, TSP, PM10, PM2.5, CO, and VOC from the iron and steel industry in the Beijing-Tianjin-Hebei region in 2015 were 388.2, 272.3, 791.9, 531.5, 386.8, 8233.8, and 265.3 kilotons, respectively, among which, sintering and pelletizing were the two processes discharging the most pollutants (17.0%-72.0%), followed by the ironmaking process (4.6%-42.4%) and the steel rolling process (3.5%-35.7%); the iron and steel industry in Tangshan discharged the most pollutants (39.1%-63.5%) among those in all the 13 cities. The impact of the iron and steel industry on the regional PM2.5 concentration was simulated by a two-layer nested meteorology-air quality coupling model system (WRF-CMAx) with Particulate Source Apportionment Technology (PSAT). The simulation results showed that the iron and steel industry contributed 14.0%, 15.9%, 12.3%, and 8.7% of the PM2.5 concentrations of the Beijing-Tianjin-Hebei region in spring, summer, autumn, and winter, respectively, and that the iron and steel industry had the most significant impact on the PM2.5 concentrations in Tangshan among all the 13 cities, with a contribution rate up to 41.2%, followed by those in Qinhuangdao, Shijiazhuang, and Handan, with contributions of 19.3%, 15.3%, and 15.1%, respectively. The iron and steel industry has an important impact on the PM2.5 concentration of the Beijing-Tianjin-Hebei region to which the government should pay more attention, and take more effective control measures to address this problem.

Characteristics and classification of PM2.5 pollution episodes in Beijing from 2013 to 2015.

During the period of 2013-2015, a total of 34 PM2.5 pollution episodes occurred in Beijing, each of which remained for at least 2days. Among that, 28 times occurred in winter half year with the average concentration of 243.1µg/m3 and summer half year with the average concentration of 194.1µg/m3. These episodes were mainly associated with lower wind speed and lower visibility as well as higher relative humidity, indicating that they belonged to heavy pollution under static stability. The PM2.5 pollution was classified into two categories according to the back trajectory analysis and meteorological background field. Category I, accounting for 22 times among all the pollution episodes, was due to air mass transport from Beijing's southern regions with north-south direction pressure gradient and sparse isopiestic. And category II was mainly led by northwestern air masses accompanied with a large area of uniform pressure field. Then, a typical case study was conducted for each category to recognize the sub-region contribution to Beijing's PM2.5 pollution based on WRF-CAMx modeling system, and the simulation results indicated that local emission source contribution decreased significantly during the accumulation phase for category I, but increased during that of category II, with an average contribution of 47.3% and 77.1% during the entire pollution period of each category, respectively. Two red alerts of air pollution occurred in December 2015 were also analyzed based on the episode classification. It was found that the second red alert pollution episode belonged to category II. The emission control measures in Beijing worked more obviously with the reduction effect ratio of 15.4% compared to the first red alert period (9.7%).