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It has been found that ozone (O3) pollution episodic case is prone to appear when the Greater Bay Area (GBA) is under the control of typhoons and sub-tropical high-pressure systems in summer. To prevent these pollutions effectively and efficiently, it’s essential to understand the contribution of O3 precursors emitted from different periods and areas under these unfavorable weather conditions. In this study, we further extended the Ozone Source Apportionment Technology (OSAT) from the Comprehensive Air Quality Model with Extensions (CAMx) model to include the function to track the emission periods of O3 precursors. Then the updated OSAT module was applied to investigate the spatial-temporal contribution of precursors emissions to the O3 concentration over the GBA in July and August 2016, when several O3 episodic cases appeared in this period. Overall, the emissions within GBA, from other regions of Guangdong province (GDo), and the neighbouring provinces are the three major contributors, which account for 23 %, 15 %, and 17 % of monthly average O3 concentration, respectively. More than 70 % of O3 in the current day is mainly formed from the pollutants emitted within 3 days and the same day’s emission contributed approximately 30 %. During the O3 episodes, when typhoon approached, more pollutants emitted 2–3 days ago from the GDo and adjacent provinces were transported to the GBA, leading to the increase of O3 in this region. Under the persistent influence of northerly wind, the pollutants originating from eastern China earlier than 2 days ago can also show an obvious impact on the O3 over the GBA in the present day, accounting for approximately 12 %. On the other hand, the O3 pollution is primarily attributed to the local emission within 2 days when the GBA is mainly under the influence of the sub-tropical high-pressure systems. These results indicated the necessity to consider the influence of meteorological conditions in implementing the control measures. Meanwhil

Trace metals in fine particulate matter (PM2.5) are of significant concern in environmental chemistry due to their toxicity and catalytic capability. An observation-constrained hybrid model is developed to resolve regional source contributions to trace metals and other primary species in PM2.5. In this method, source contributions to primary PM2.5 (PPM2.5) from the Community Multiscale Air Quality (CMAQ) Model at each monitoring location are improved to align better with the observation data by applying source-specific scaling factors estimated from a unique regression model. The adjusted PPM2.5 predictions and chemical speciation data are then used to generate observation-constrained source profiles of primary species. Finally, spatial distributions of their source contributions are produced by multiplying the improved CMAQ PPM2.5 contributions with the deduced source profiles. The model is applied to the Pearl River Delta (PRD) region, China using daily observation data collected at multiple stations in 2015 to resolve source contributions to 8 trace metals, elemental carbon, primary organic carbon, and 10 other primary species. Compared to three previous methods, the new model predicts 13 species with smaller model errors and 16 species with less model biases in 10-fold cross validation analysis. The source profiles determined in this study also show good agreement with those collected from the literature. The new model shows that during 2015 in the PRD region, Cu is mainly from the area sources (31%), industry sector (27%), and power generation (20%), with an annual average concentration as high as 50 ng m−3 in some districts. Meanwhile, major contributors to Mn are area sources (40%), emission from outside PRD (23%) and power generation (17%), leading to a mean level of around 10 ng m−3. Such information is essential in assessing the epidemiological impacts of trace metals as well as formulating effective control measures to protect public health.

Nitrogen oxides (NOx, mainly comprising NO and NO2) is the essential precursor of secondary air pollutants, such as ozone and particulate nitrate. To better understand NOx emission levels and acquire reasonable simulation results for further analysis, a reasonable emission inventory is needed. In this study, a new method, combining the three-dimensional chemical transport model simulation, surface NO2 observations, the three-dimensional variational 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 and normalized mean bias decreased by approximately 40% and 60% in the NO2 simulation using posterior NOx emissions. Compared with the emissions for 2015, the NOx emission generally reduced 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. It can provide policymakers and academic researchers with the latest emission information for better emission control and air pollution research.

Deep-learning frameworks can effectively forecast the air pollution data for individual stations by decoding time series data. However, most of the existing time-series-based deep-learning models use offline spatial interpolation strategies and thus cannot reliably project the station-based forecast to the spatial region of interest. In this study, the station-based long short-term memory (LSTM) technique was extended for spatial air quality forecasting by combining a novel deep-learning layer, termed the broadcasting layer, which incorporates a learnable weight decay parameter designed for point-to-area extension. Unlike most existing deep-learning-based methods that isolate the interpolation from the model training process, the proposed end-to-end LSTM broadcasting framework can consider the temporal characteristics of the time series and spatial relationships among different stations. To validate the proposed deep-learning framework, PM2.5 and O3 forecasts for the next 48 h were obtained using 3D chemical transport model simulation results and ground observation data as the inputs. The root mean square error associated with the proposed framework was 40 % and 20 % lower than those of the Weather Research and Forecasting-Community Multiscale Air Quality model and an offline combination of the deep-learning and spatial interpolation methods, respectively. The novel LSTM broadcasting framework can be extended for air pollution forecasting in other regions of interest.

As one of the largest agglomerations of cities in the world, the infrastructure and living conditions of the residents in the Pearl River Delta (PRD) have significantly improved since the late 1970s. However, as a two-sided sword, the boosting of economic growth has inevitably exerted adverse effects on the environment in this region. To further understand the current fine particulate matter (PM2.5) pollution sources in this region and formulate an effective future air pollution control strategy for it, a hybrid source apportionment method was applied to adjust source estimates of secondary inorganic aerosols (SIAs) [particulate sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+)] by combining Comprehensive Air Quality Model Extensions and measurement data from 19 sampling sites covering the PRD region in 2015. After correction by the hybrid method, the normalized mean errors for the three major species SO42−, NO3−, and NH4+ in PM2.5 simulated by the model decreased from 0.412, 1.261, and 0.401 to 0.321, 0.565, and 0.130, respectively. Tianhu, Taishan, Taipa Grande, whose secondary inorganic aerosol concentration variations are −48.33%, −40.59%, and 39.10% after adjustment, respectively, are the three top sites with the most substantial modification. Our study revealed that the emissions outside the PRD region are still the most important contributor to the three components after the correction. In most Hong Kong sites, the adjusted mobile source is subordinate to the cross-boundary transport and became the largest endogenous source. In contrast to that in Hong Kong, preceded only by super-regional sources, the outstanding contributor to SIAs in Guangdong and Macao is the area source. Our results indicated that besides collaborative control measures, the Hong Kong and Guangdong government should emphasize the emissions from motor vehicles and residential sources, respectively. Overall, this hybrid source apportionment approach can narrow down the discrepancy betwee

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.

PM2.5 pollution has adverse health effects on humans. Urbanization and long-term meteorological variations play important roles in influencing the PM2.5 concentration and its associated health effects. Our results indicate that the urbanization process can enhance the PM2.5 concentration globally. The PM2.5-caused mortality density (deaths/100 km2) is also positively correlated with the urbanization degree in both developed and developing countries. The results from machine learning technique revealed that the meteorology-driven variation in PM2.5-caused health burden has increased with the increase in the urbanization degree from 1980 to 2018, suggesting that residents living in urban areas are more vulnerable to experiencing unfavorable meteorological conditions (e.g. low wind speed and planetary boundary layer height). The maximum difference in PM2.5-caused mortality due to the variation in annual meteorological conditions (between 2013 and 1986) was 270 600 (196 800–317 900). Our findings indicate an urgent need to understand the driving force behind the appearance of unfavorable meteorological situations and propose suitable climate mitigation measures.

Wet deposition of non-sea-salt sulfate (nss-SO42-) and nitrate (NO3-), derived from anthropogenic emissions of SO2 and NOx, exerts adverse effects on ecosystems. In this work, an ensemble back-propagation neural network was proposed to estimate the long-term wet depositions of nss-SO42- (2005-2017) and NO3- (2001-2014) over East Asia in 10-km resolution. The R2 values for the 10-fold cross-validation of annual wet depositions of nss-SO42- and NO3- were 0.90 and 0.85, respectively. The hotspots of the wet deposition of these two acidic species span southwestern, central, and eastern China. The molar ratio of NO3- to nss-SO42- increased in 10 out of 12 analyzed East Asian countries from 2005 to 2014, which indicates that the acidity in rainwater shifts from sulfur type to nitrogen type over most of the regions. The wet deposition on the four ecosystems (forest, grassland, cropland, and freshwater body) was also analyzed. Results showed that the nss-SO42- wet deposition on 25.5% of freshwater bodies in 2015 and NO3- wet deposition on 21.7% of grassland in 2014 exceeded the ecosystem empirical critical loads (25 kg/ha sulfate and 2 kg N/ha) in East Asia. Thus, more stringent and regionally collaborative sulfur and nitrogen emission-control measures are urgently needed to protect the ecosystem of East Asia.

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.

In this study, the trend of PM2.5 concentrations and its adverse health effects in China from 2001 to 2017 are estimated utilizing 1-km high-resolution annual satellite-retrieved PM2.5 data. PM2.5 concentrations for most of the provinces/cities remained stable from 2001 to 2012; however, following the issue of the Air Pollution Prevention and Control Action Plan (APPCAP) by the central government of China, a dramatic decrease in PM2.5 concentrations from 2013 to 2017 occurred. Premature mortality caused by PM2.5 dropped from 1,078,800 in 2014 to 962,900 in 2017. The PM2.5 caused 17-year average mortality ranges from 3800 in Hainan Province to 124,800 in Henan Province. The health cost benefits gained by the reduction of PM2.5 pollution amounted to US $193,800 in 2017 (compared to the costs due to PM2.5 concentrations in 2013), amounting to 1.58% of the total national GDP. The impacts of urbanization on PM2.5 concentration and mortality are analyzed. The PM2.5 concentration and its induced mortality density in dense urban areas are much higher than those in rural areas. The aggravation of PM2.5 associated premature mortality in urban areas is mainly due to the larger amount of emissions and to urban migration, and 6500 deaths in 2014 could have been avoided were the population ratios in dense-urban/normal-urban/rural areas to be reversed to the ones in 2001. It is recommended that people with respiratory-related diseases live in rural areas, where the pollutant concentration is relatively low.

PM2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM2.5 levels exceeding the IT-3 standard during the pollution episodes.