Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China)

Role of microglia polarization induced by glucose metabolism disorder in the cognitive impairment of mice from PM2.5 exposure

Studies have found that PM2.5 can damage the brain, accelerate cognitive impairment, and increase the risk of developing a variety of neurodegenerative diseases. However, the potential molecular mechanisms by which PM2.5 causes learning and memory problems are yet to be explored. In this study, we evaluated the neurotoxic effects in mice after 12 weeks of PM2.5 exposure, and found that this exposure resulted in learning and memory disorders, pathological brain damage, and M1 phenotype polarization on microglia, especially in the hippocampus. The severity of this damage increased with increasing PM2.5 concentration. Proteomic analysis, as well as validation results, suggested that PM2.5 exposure led to abnormal glucose metabolism in the mouse brain, which is mainly characterized by significant expression of hexokinase, phosphofructokinase, and lactate dehydrogenase. We therefore administered the glycolysis inhibitor 2-deoxy-d-glucose (2-DG) to the mice exposed to PM2.5, and showed that inhibition of glycolysis by 2-DG significantly alleviated PM2.5-induced hippocampal microglia M1 phenotype polarization, and reduced the release of inflammatory factors, improved synaptic structure and related protein expression, which alleviated the cognitive impairment induced by PM2.5 exposure. In summary, our study found that abnormal glucose metabolism-mediated inflammatory polarization of microglia played a role in learning and memory disorders in mice exposed to PM2.5. This study provides new insights into the neurotoxicity caused by PM2.5 exposure, and provides some theoretical references for the prevention and control of cognitive impairment induced by PM2.5 exposure.

Fine particulate matter disrupts bile acid homeostasis in hepatocytes via binding to and activating farnesoid X receptor

Fine particulate matter (PM2.5)-induced metabolic disorders have attracted increasing attention, however, the underlying molecular mechanism of PM2.5-induced hepatic bile acid disorder remains unclear. In this study, we investigated the effects of PM2.5 components on the disruption of bile acid in hepatocytes through farnesoid X receptor (FXR) pathway. The receptor binding assays showed that PM2.5 extracts bound to FXR directly, with half inhibitory concentration (IC50) value of 21.7 μg/mL. PM2.5 extracts significantly promoted FXR-mediated transcriptional activity at 12.5 μg/mL. In mouse primary hepatocytes, we found PM2.5 extracts (100 μg/mL) significantly decreased the total bile acid levels, inhibited the expression of bile acid synthesis gene (Cholesterol 7 alpha-hydroxylase, Cyp7a1), and increased the expression of bile acid transport genes (Multidrug resistance associated protein 2, Abcc2; and Bile salt export pump, Abcb11). Moreover, these alterations were significantly attenuated by knocking down FXR in hepatocytes. We further divided the organic components and water-soluble components from PM2.5, and found that two components bound to and activated FXR, and decreased the bile acid levels in hepatocytes. In addition, benzo[a]pyrene (B[a]P) and cadmium (Cd) were identified as two bioactive components in PM2.5-induced bile acid disorders through FXR signaling pathway. Overall, we found PM2.5 components could bind to and activate FXR, thereby disrupting bile acid synthesis and transport in hepatocytes. These new findings also provide new insights into PM2.5-induced toxicity through nuclear receptor pathways.

Estimating future PM2.5-attributed acute myocardial infarction incident cases under climate mitigation and population change scenarios in Shandong Province, China

BACKGROUND

The effects of fine particulate matter (PM2.5) on acute myocardial infarction (AMI) have been widely recognized. However, no studies have comprehensively evaluated future PM2.5-attributed AMI burdens under different climate mitigation and population change scenarios. We aimed to quantify the PM2.5-AMI association and estimate the future change in PM2.5-attributed AMI incident cases under six integrated scenarios in 2030 and 2060 in Shandong Province, China.

METHODS

Daily AMI incident cases and air pollutant data were collected from 136 districts/counties in Shandong Province from 2017 - 2019. A two-stage analysis with a distributed lag nonlinear model was conducted to quantify the baseline PM2.5-AMI association. The future change in PM2.5-attributed AMI incident cases was estimated by combining the fitted PM2.5-AMI association with the projected daily PM2.5 concentrations under six integrated scenarios. We further analyzed the factors driving changes in PM2.5-related AMI incidence using a decomposition method.

RESULTS

Each 10 μg/m3 increase in PM2.5 exposure at lag05 was related to an excess risk of 1.3 % (95 % confidence intervals: 0.9 %, 1.7 %) for AMI incidence from 2017 - 2019 in Shandong Province. The estimated total PM2.5-attributed AMI incident cases would increase by 10.9-125.9 % and 6.4-244.6 % under Scenarios 1 - 3 in 2030 and 2060, whereas they would decrease by 0.9-5.2 % and 33.0-46.2 % under Scenarios 5 - 6 in 2030 and 2060, respectively. Furthermore, the percentage increases in PM2.5-attributed female cases (2030: -0.3 % to 135.1 %; 2060: -33.2 % to 321.5 %) and aging cases (2030: 15.2-171.8 %; 2060: -21.5 % to 394.2 %) would wholly exceed those in male cases (2030: -1.8 % to 133.2 %; 2060: -41.1 % to 264.3 %) and non-aging cases (2030: -41.0 % to 45.7 %; 2060: -89.5 % to -17.0 %) under six scenarios in 2030 and 2060. Population aging is the main driver of increased PM2.5-related AMI incidence under Scenarios 1 - 3 in 2030 and 2060, while improved air quality can offset these negative effects of population aging under the implementation of the carbon neutrality and 1.5 °C targets.

CONCLUSION

The combination of ambitious climate policies (i.e., 1.5 °C warming limits and carbon neutrality targets) with stringent clean air policies is necessary to reduce the health impacts of air pollution in Shandong Province, China, regardless of population aging.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM2.5 concentrations in adjacent areas weakened the temporal convergence of PM2.5 concentrations. This paper introduced policy implications for haze prevention and control.

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.

Analysis of PM2.5 Variations Based on Observed, Satellite-Derived, and Population-Weighted Concentrations

Fine particulate matter (PM2.5), which can cause adverse human health effects, has been proven as the first air pollutant in China. In situ observations with ground-level monitoring and satellite-based concentrations have been used to analyze the variations in PM2.5. However, variation analyses based on these two kinds of measurement have mainly focused on the concentration itself and ignored the effects on the population. Therefore, this study not only investigated these two kinds of measurements, but also performed weighted population analyses to study the variations in PM2.5. Firstly, daily models of timely structure adaptive modeling (TSAM) were constructed to simulate satellite-derived PM2.5 levels from January 2013 to December 2016. Secondly, population-weighted concentrations were calculated based on TSAM-derived PM2.5 surfaces. Finally, observed, TSAM-derived, and population-weighted concentrations were used to analyze the variations in PM2.5. The results showed the different importance of various input parameters; AOD had the highest rank. Additionally, TSAM models demonstrated good performance, fitting R ranging from 0.86 to 0.91, and validating R from 0.82 to 0.89. According to the air quality standard in China, TSAM-derived PM2.5 showed that the increase in area lower than Level II was 29.03% and the increase in population was only 14.81%. This indicates that the air quality exhibited an overall improvement in spatial perspective, but some areas with high population density showed a relatively low improvement due to uneven distributions in China. The population-weighted PM2.5 concentration could better represent the health threats of air pollutants compared with in situ observations.

Random Forest Estimation and Trend Analysis of PM2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) of the modeling was 0.85, the root means square error (RMSE) was 14.63 μg/m3, and the mean absolute error (MAE) was 10.03 μg/m3. The quality assessment of the synthesized yearly PM2.5 concentration dataset shows that R2 = 0.77, RMSE = 6.92 μg/m3, and MAE = 5.42 μg/m3. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a high-spatial resolution (i.e., 1 km) concentration retrieval model based on the GLM and BME method was developed to obtain the daily spatiotemporal characteristics of GOCs. The hourly ozone records provided by the national air quality monitoring stations and multiple geospatial datasets were used as input data. The model exhibited satisfactory performance (R2 = 0.75, RMSE = 10.86 µg/m3). The derived GOCs show that the ozone levels at gasoline stations and their adjacent areas (1~3 km away from the gasoline stations) were significantly higher than the citywide average level, and this phenomenon gradually eased with the increasing distance from the gasoline stations. The findings indicate that special attention should be given to the prevention and control of ground-level ozone exposure risks in human settlements and activity areas near gasoline stations.

Short-term air quality forecasting model based on hybrid RF-IACA-BPNN algorithm

Despite the apparent improvement in air quality in recent years through a series of effective measures, the concentration of PM_2.5 and O₃ in Chengdu city remains high. And both the two pollutants can cause serious damage to human health and property; consequently, it is imperative to accurately forecast hourly concentration of PM_2.5 and O₃ in advance. In this study, an air quality forecasting method based on random forest (RF) method and improved ant colony algorithm coupled with back-propagation neural network (IACA-BPNN) are proposed. RF method was used to screen out highly correlated input variables, and the improved ant colony algorithm (IACA) was adopted to combine with BPNN to improve the convergence performance. Two datasets based on two different kinds of monitoring stations along with meteorological data were applied to verify the performance of this proposed model and compared with another five plain models. The results showed that the RF-IACA-BPNN model has the minimum statistical error of the mean absolute error, root mean square error, and mean absolute percentage error, and the values of R² consistently outperform other models. Thus, it is concluded that the proposed model is suitable for air quality prediction. It was also detected that the performance of the models for the forecasting of the hourly concentrations of PM_2.5 were more acceptable at suburban station than downtown station, while the case is just the opposite for O₃, on account of the low variability dataset at suburban station.

Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020

Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks.

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m³ increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Spatiotemporal variations of air pollution and population exposure in Shandong Province, eastern China, 2014-2018

To clarify the characteristics and interannual variation of air pollution since the implementation of China's clean air actions, hourly in situ measurements of six gaseous and particulate criteria pollutants at 100 sites in Shandong Province were studied during 2014-2018. General decreasing trends in the concentrations of PM_2.5, PM₁₀, NO₂, SO₂, and CO were observed, while O₃ increased continuously. In 2018, the annual average PM_2.5, PM₁₀, NO₂, SO₂, and CO concentration in Shandong was 50, 100, 35, 16 μg m^-3, and 1.5 mg m^-3, representing decreases of 39%, 30%, 24%, 73%, and 35% from 2014, respectively. These decreases occurred throughout the province. Seven "2 + 26" cities (in Beijing-Tianjin-Hebei and its surrounds) in western Shandong had higher average concentrations and greater reductions than other areas. In contrast, O₃ concentration rose, with occurrences of the 90^th percentile of all daily maximum 8-h averages increasing by 12% from 159 to 181 μg m^-3, during 2014-2018. From May to September, O₃ pollution dominated as the sole primary pollutant on non-attainment days, and PM_2.5 contributed to more than 90% of polluted days in wintertime months. Population exposures were investigated based on high-resolution monitoring data and population distribution, and high exposure to pollution was displayed. The population-weighted exposure to PM_2.5 in Shandong was 50 μg m^-3, a decrease of 33%. Eighty-nine percentage of the provincial population was exposed to PM_2.5 > 35 μg m^-3, while for 99.2% of population in the seven "2 + 26" cities, PM_2.5 exposure exceeded 50 μg m^-3.

Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China

PM_2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM_2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R² which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM_2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results.

Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data

Improving the spatiotemporal coverage of remote sensing (RS) products, such as sea surface chlorophyll concentration (SSCC), can offer a better understanding of the spatiotemporal SSCC distribution for ocean management purposes. In the first part of this work, 834 in-situ SSCC measurements of the SeaBASS-NASA (National Aeronautics and Space Administration) during 2002-2016 served as the empirical dataset. A moving window with ±3 days and ±0.5° centered at each of the in-situ SSCC measurements established a search neighborhood for Moderate Resolution Imaging Spectroradiometer Level 2 (MODIS L2) SSCC and MODIS L2 sea surface temperature (SST) data, and the matched SSCC and SST data were used for building a linear SSCC-SST relationship. The unmatched SST was introduced to the linear model for generating soft SSCC data with uniform distributions. The inherent spatiotemporal dependency of the SSCC distribution was then represented by the Bayesian maximum entropy (BME) method, which incorporated the soft SSCC data as auxiliary variable for SSCC estimation and mapping purposes. The results showed that a 75.3% accuracy improvement of remote SSCC retrieval in terms of R² can be achieved by BME-based method compared to the original MODIS L2 product. Subsequently, the BME-based method was applied to obtain daily SSCC dataset in Chesapeake Bay (USA) during the period 2010-2019. It was found that the SSCC distribution exhibited a decreasing spatial trend from the upper bay to the outer bay, whereas decreasing and increasing temporal trends were detected during the periods 2011-2014 and 2016-2019, respectively. The generalized Cauchy process was used to quantitatively describe the autocorrelation SSCC function in the Chesapeake Bay. The results showed that the outer bay exhibited the strongest long-range dependence among the four sub-regions, whereas the middle bay exhibited the weakest long-range dependence. Finally, one-point and two-point stochastic site indicators (SSIs) were employed to explore the spatiotemporal SSCC characteristics in Chesapeake Bay. The one-point SSI results showed that nearly 100% of the upper, middle and the lower bay areas experienced a high SSCC level (>5 mg/m³) during the entire study period. The area with SSCC >5 mg/m³ in the outer bay increased a lot during the winter season, but the area with SSCC >10 or 20 mg/m³ decreased significantly in the upper, middle and lower bay. Simultaneously, the SSCC dispersion in these areas was rather small during the winter season. On the other hand, the two-point SSI results showed that although the SSCC levels differ among the four sub-regions, but the SSCC connectivity structures between pairs of points also displayed some similarities in terms of their spatiotemporal dependency. In conclusion, the proposed BME-based method was shown to be a promising remote SSCC mapping technique that exhibited a powerful ability to improve both accuracy and coverage of RS products. The SSIs can be also used to explore the spatiotemporal characteristics of a variety of natural attributes in waters.

When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Spatial-Temporal Distribution Variation of Ground-Level Ozone in China's Pearl River Delta Metropolitan Region

Long-term exposure to ozone pollution will cause severe threats to residents' physical and mental health. Ground-level ozone is the most severe air pollutant in China's Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial-temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD's 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial-temporal kriging (STK) interpolation, and the STK model's simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone's spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.

The distribution and drivers of PM2.5 in a rapidly urbanizing region: The Belt and Road Initiative in focus

The accelerating urbanization has led to serious air pollution dominated by PM_2.5, posing a critical challenge for the environmental sustainability of the Belt and Road Initiative (BRI). However, a focus on the distribution and drivers of PM_2.5 concentrations in BRI is lacking. To fill in the gap, this study explores the spatio-temporal distribution of PM_2.5 concentrations in 74 nations partnering the BRI and identifies the socioeconomic and natural drivers behind the variation through the joint use of spatial autocorrelation and regression analyses. We find that the PM_2.5 concentrations of BRI show significant spatial autocorrelation and spatial heterogeneity on the national scale. The most heavily polluted regions are observed mainly in China, Southeast Asia, South Asia, West Asia and North Africa, particularly in the Arabian Gulf region. Energy intensity and per capita electricity consumption act as the major drivers of the PM_2.5 concentrations, whereas the expanding forest area contributes to the decrease in PM_2.5 concentrations notably. Our findings highlight the need for speeding up new-type urbanization as part of the green BRI practice, calling for international cooperation and coordinated action aimed at enhancing synergies of air-quality and climate policies that at present are mostly launched and implemented in isolation. From a broader point of view, in struggling towards BRI's cleaner air, more attention should be paid to creating policy synergies between the green BRI, the Paris Agreement, and the United Nations 2030 Agenda for Sustainable Development.

Characteristics of Ozone Pollution, Regional Distribution and Causes during 2014–2018 in Shandong Province, East China

The summer ozone pollution of Shandong province has become a severe problem in the period 2014–2018. Affected by the monsoon climate, the monthly average ozone concentrations in most areas were unimodal, with peaks in June, whereas in coastal areas the concentrations were bimodal, with the highest peak in May and the second highest peak in September. Using the empirical orthogonal function method, three main spatial distribution patterns were found. The most important pattern proved the influences of solar radiation, temperature, and industrial structure on ozone. Spatial clustering analysis of the ozone concentration showed Shandong divided into five units, including Peninsula Coastal area (PC), Lunan inland area (LN), Western Bohai area (WB), Luxi plain area (LX), and Luzhong mountain area (LZ). Influenced by air temperature and local circulation, coastal cities had lower daytime and higher nighttime ozone concentrations than inland. Correlation analysis suggested that ozone concentrations were significantly positively correlated with solar radiation. The VOCs from industries or other sources (e.g., traffic emission, petroleum processing, and chemical industries) had high positive correlations with ozone concentrations, whereas NOx emissions had significantly negatively correlation. This study provides a comprehensive understanding of ozone pollution and theoretical reference for regional management of ozone pollution in Shandong province.

Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016

China is experiencing severe PM 2 . 5 (fine particles with a diameter of 2.5 μ g or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM 2 . 5 concentration variation in China during the period 1999–2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM 2 . 5 concentration during the last 18 years in the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM 2 . 5 pollution, we find that Tarim Basin endures a high PM 2 . 5 concentration, and this should have some relationship with oil exploration. The relationship between PM 2 . 5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.

Space‐Time Characterization and Risk Assessment of Nutrient Pollutant Concentrations in China's Near Seas

Human activities interacting with coastal waters lead to large amounts of nutrient loading and severe water pollution in China's near Seas. In this context, a comprehensive quantitative characterization of the spatiotemporal variation of nutrient pollutant concentrations is a key component of any reliable seawater quality assessment and integrated coastal management plan. The present work combines the Bayesian maximum entropy method with stochastic site indicators to estimate monthly nitrate and phosphate concentrations in China's near seas during 2015, explore their spatiotemporal variation, and provide an explicit quantitative assessment of seawater quality in conditions of in situ uncertainty. This makes it the first study of space‐time nutrient pollutant characterization at a national‐scale in a coastal seawater environment. The results showed that nitrate and phosphate distributions exhibit the same spatial trends along China's near seas, whereas high nutrient pollution levels are found in the Yangtze River, Liaohe River, and Pearl River estuaries. Local differences of temporal trends exist between nitrate and phosphate distributions, which suggest that distinct remediation strategies are needed to properly satisfy the required seawater quality standards. The average nitrate and phosphate concentrations across space‐time were found to be equal to 0.271 and 0.015 mg/L, respectively. The nitrate and phosphate concentrations exceeding the fourth grade seawater quality standard during 2015 were about 11% and 2.6%, respectively. The study of both the seasonal changes in human activities along the coastal cities and the temporal marine hydrodynamics can offer a better understanding of the seawater quality and the biogeochemical process of nutrient transport and distribution.

Pollution Characterization and Source Apportionment of Day and Night PM2.5 Samples in Urban and Suburban Communities of Tianjin (China)

Day and night PM_2.5 samples were collected from two typical urban and suburban communities in Tianjin. The major chemical components in PM_2.5, including the metal elements, polycyclic aromatic hydrocarbons (PAHs), and inorganic water-soluble ions, were monitored. A positive matrix factorization (PMF) model was used to apportion the potential sources of PM_2.5 at the two sites in the daytime and nighttime. The results indicated that the PM_2.5 concentration was higher in the suburban area than in the urban area during the daytime in winter. The daytime and nighttime PAHs concentrations at both sites were both generally higher in winter than in summer. The concentrations of some of the metal elements were higher in summer than in winter. Regional differences and day and night differences in the metals and water-soluble ions commonly existed. The PMF analysis indicated that coal combustion and transportation-related sources were the predominant sources in the urban and suburban areas in the daytime in winter, and secondary aerosols were the most important source for the suburban area in the nighttime in winter. There were more pollution sources of PM_2.5 during the daytime in summer, especially in the suburban area. In the nighttime in summer, the pollution sources of PM_2.5 in the urban and suburbs areas were basically the same, but the source apportionment was quite different.

Spatio-temporal variations and factors of a provincial PM2.5 pollution in eastern China during 2013-2017 by geostatistics

Fine particulate matter (PM_2.5) is a typical air pollutant and has adverse health effects across the world, especially in the rapidly developing China due to significant air pollution. The PM_2.5 pollution varies with time and space, and is dominated by the locations owing to the differences in geographical conditions including topography and meteorology, the land use and the characteristics of urbanization and industrialization, all of which control the pollution formation by influencing the various sources and transport of PM_2.5. To characterize these parameters and mechanisms, the 5-year PM_2.5 pollution patterns of Jiangsu province in eastern China with high-resolution was investigated. The Kriging interpolation method of geostatistical analysis (GIS) and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were conducted to study the spatial and temporal distribution of air pollution at 110 sites from national air quality monitoring network covering 13 cities. The PM_2.5 pollution of the studied region was obvious, although the annual average concentration decreased from previous 72 to recent 50 μg m⁻³. Evident temporal variations showed high PM_2.5 level in winter and low in summer. Spatially, PM_2.5 level was higher in northern (inland, heavy industry) than that in eastern (costal, plain) regions. Industrial sources contributed highest to the air pollution. Backward trajectory clustering and potential source contribution factor (PSCF) analysis indicated that the typical monsoon climate played an important role in the aerosol transport. In summer, the air mass in Jiangsu was mainly affected by the updraft from near region, which accounted for about 60% of the total number of trajectories, while in winter, the long-distance transport from the northwest had a significant impact on air pollution.

Probabilistic logic analysis of the highly heterogeneous spatiotemporal HFRS incidence distribution in Heilongjiang province (China) during 2005-2013

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by hantavirus (belongs to Hantaviridae family). A large amount of HFRS cases occur in China, especially in the Heilongjiang Province, raising great concerns regarding public health. The distribution of these cases across space-time often exhibits highly heterogeneous characteristics. Hence, it is widely recognized that the improved mapping of heterogeneous HFRS distributions and the quantitative assessment of the space-time disease transition patterns can advance considerably the detection, prevention and control of epidemic outbreaks.

METHODS

A synthesis of space-time mapping and probabilistic logic is proposed to study the distribution of monthly HFRS population-standardized incidences in Heilongjiang province during the period 2005-2013. We introduce a class-dependent Bayesian maximum entropy (cd-BME) mapping method dividing the original dataset into discrete incidence classes that overcome data heterogeneity and skewness effects and can produce space-time HFRS incidence estimates together with their estimation accuracy. A ten-fold cross validation analysis is conducted to evaluate the performance of the proposed cd-BME implementation compared to the standard class-independent BME implementation. Incidence maps generated by cd-BME are used to study the spatiotemporal HFRS spread patterns. Further, the spatiotemporal dependence of HFRS incidences are measured in terms of probability logic indicators that link class-dependent HFRS incidences at different space-time points. These indicators convey useful complementary information regarding intraclass and interclass relationships, such as the change in HFRS transition probabilities between different incidence classes with increasing geographical distance and time separation.

RESULTS

Each HFRS class exhibited a distinct space-time variation structure in terms of its varying covariance parameters (shape, sill and correlation ranges). Given the heterogeneous features of the HFRS dataset, the cd-BME implementation demonstrated an improved ability to capture these features compared to the standard implementation (e.g., mean absolute error: 0.19 vs. 0.43 cases/105 capita) demonstrating a point outbreak character at high incidence levels and a non-point spread character at low levels. Intraclass HFRS variations were found to be considerably different than interclass HFRS variations. Certain incidence classes occurred frequently near one class but were rarely found adjacent to other classes. Different classes may share common boundaries or they may be surrounded completely by another class. The HFRS class 0-68.5% was the most dominant in the Heilongjiang province (covering more than 2/3 of the total area). The probabilities that certain incidence classes occur next to other classes were used to estimate the transitions between HFRS classes. Moreover, such probabilities described the dependency pattern of the space-time arrangement of HFRS patches occupied by the incidence classes. The HFRS transition probabilities also suggested the presence of both positive and negative relations among the main classes. The HFRS indicator plots offer complementary visualizations of the varying probabilities of transition between incidence classes, and so they describe the dependency pattern of the space-time arrangement of the HFRS patches occupied by the different classes.

CONCLUSIONS

The cd-BME method combined with probabilistic logic indicators offer an accurate and informative quantitative representation of the heterogeneous HFRS incidences in the space-time domain, and the results thus obtained can be interpreted readily. The same methodological combination could also be used in the spatiotemporal modeling and prediction of other epidemics under similar circumstances.

Assessing the severe eutrophication status and spatial trend in the coastal waters of Zhejiang province (China)

The eutrophication of the coastal waters of Zhejiang Province has become one of the main contamination threats to the region's coastal marine ecosystems. Accordingly, the comprehensive characterization of the eutrophication status in terms of improved quantitative methods is valuable for local risk assessment and policy making. A novelty of this work is that the spatial distributions of chemical oxygen demand, dissolved inorganic nitrogen, and dissolved inorganic phosphorus were estimated across space by the Bayesian maximum entropy (BME) method. The BME estimates were found to have the best cross‐validation performance compared to ordinary kriging and inverse distance weighted techniques. Based on the BME maps, it was found that about 25.95%, 19.18%, 20.53%, and 34.34% of these coastal waters were oligotrophic, mesotrophic, eutrophic, and hypereutrophic. Another novelty of the present work is that comprehensive stochastic site indicators (SSI) were introduced in the quantitative characterization of the eutrophication risk in the Zhejiang coastal waters under conditions of in situ uncertainty. The results showed that the level of the eutrophication index (EI) increased almost linearly with increasing threshold values; and that 71%, 51%, and 19% of coastal locations separated by various spatial lags experience considerable mesotrophic, eutrophic, and hypereutrophic risks, respectively. The average EI values over the subregions of the Zhejiang coastal waters graded as “oligotrophic or higher,” “eutrophic or higher,” and “hypereutrophic” were about 11.14, 14.28, and 25.34, respectively. Our results also revealed that the joint eutrophication strength between coastal locations in the Zhejiang region was consistently greater than the combined strength of independent eutrophications at these locations (we termed this situation “positive quadrant eutrophication dependency”). It was found that a critical eutrophication threshold ζcr ≈ 8.38 exists so that below ζcr the spatial eutrophication dependency in the Zhejiang coastal waters increases with ζ, whereas above ζcr the opposite is true. Moreover, the eutrophication dependency decreases as the separation distance δs increases. Interestingly, at distances δs smaller than a critical distance δscr ≈ 15  km, the eutrophication locations are concentrated in the coastal waters of the Zhejiang province rather than being dispersed (this observation holds even for large thresholds ζ). Elasticity analysis of eutrophication indicators offered a quantitative measure of the excess eutrophication change in the Zhejiang coastal waters caused by a threshold change (the larger the elasticity is, the more sensitive eutrophication is to threshold changes). The above findings can contribute to an improved understanding of seawater quality and provide a practical approach for the identification of critical coastal water regions.

Heavy metal contamination assessment of surface sediments of the East Zhejiang coastal area during 2012-2015

This work is the first systematic quantitative analysis of the heavy metal situation along the Zhejiang coastal region focusing on the integrative assessment of the concentrations of seven heavy metals (Cu, Cd, Hg, Zn, Pb, Cr, and As) in surface sediments during the 2012-2015 period. Different heavy metal contamination indices were used for surface sediment quality assessment purposes. The numerical results revealed a noticeable spatial fluctuation of the degree of contamination throughout the region during the four years considered. Higher contamination levels and ecological risks were detected in the southern part of the Zhejiang coastal region. It was found that the Cu, Cd and Hg were the predominant contaminants along the Zhejiang coast with mean regional concentrations varying between 29.1 and 34.2, 0.12-0.17, and 0.044-0.052 mg/kg, respectively. The Cr and Pb exhibited lower contamination levels than the other metals during each one of the years 2012-2015. Stochastic site indicators of heavy metal contamination were used to assess regional uncertainties and obtain useful physical interpretations of the state of contamination of the Zhejiang coast. These indicators can be expressed explicitly in terms of probabilities of heavy metal contamination (either at a global scale or spatially distributed over the coastal region), and therefore they can be considered as risk indicators. It was found that the fraction of the coastal region where excess contamination occurred could never exceed the ratio of the mean heavy metal contamination over the selected threshold. In half of the coast study region, the degree of heavy metal contamination was higher than the median spatial contamination values during the month of August of the years 2012-2015. The spatial means of excess contamination and excess differential contamination increased as the relative area of over-contamination increased. Within the substantially contaminated sub-region of the Zhejiang coast, stronger contamination correlations were observed between locations separated by shorter distances. These correlations were higher when smaller thresholds were considered. As regards the spatial connectivity of the corresponding contamination risks, it was found that 44%, 31%, 39% and 63% of the location pairs in the Zhejiang coast simultaneously experienced moderate risks during the years 2012, 2013, 2014 and 2015, respectively. The ratio of the probability of excess contamination at both locations separated by distances < 20 km over the probability of excess contamination at either one of these two locations was high even for large thresholds, indicating that locations with high contamination are concentrated rather than being dispersed along the Zhejiang coast. Lastly, another interesting finding is that the characterization of the Zhejiang coastal region as over-contaminated is very sensitive to the DC threshold considered, that is, a small increase in the threshold selected can reduce significantly the probability that region is characterized as over-contaminated.

Space-time PM2.5 mapping in the severe haze region of Jing-Jin-Ji (China) using a synthetic approach

Long- and short-term exposure to PM_2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM_2.5 exposure during the period January 10-31, 2013. Technically, the processing of large space-time PM_2.5 datasets and the mapping of the space-time distribution of PM_2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM_2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms the PM_2.5 concentration values from the original spatiotemporal domain onto a spatial domain that moves along the direction of the PM_2.5 velocity spread. An interesting methodological feature of the synthetic approach is that its components (methods or models) are complementary, i.e., one component can compensate for the occasional limitations of another component. Insight is gained in terms of a PM_2.5 case study covering the severe haze Jing-Jin-Ji region during October 1-31, 2015. The proposed synthetic approach explicitly accounted for physical space-time dependencies of the PM_2.5 distribution. Moreover, the assimilation of auxiliary information and the dimensionality reduction achieved by the synthetic approach produced rather impressive results: It generated PM_2.5 concentration maps with low estimation uncertainty (even at counties and villages far away from the monitoring stations, whereas during the haze periods the uncertainty reduction was over 50% compared to standard PM_2.5 mapping techniques); and it also proved to be computationally very efficient (the reduction in computational time was over 20% compared to standard mapping techniques).

Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively.

A framework for delineating the regional boundaries of PM2.5 pollution: A case study of China

Fine particulate matter (PM_2.5) pollution has been a major issue in many countries. Considerable studies have demonstrated that PM_2.5 pollution is a regional issue, but little research has been done to investigate the regional extent of PM_2.5 pollution or to define areas in which PM_2.5 pollutants interact. To allow for a better understanding of the regional nature and spatial patterns of PM_2.5 pollution, This study proposes a novel framework for delineating regional boundaries of PM_2.5 pollution. The framework consists of four steps, including cross-correlation analysis, time-series clustering, generation of Voronoi polygons, and polygon smoothing using polynomial approximation with exponential kernel method. Using the framework, the regional PM_2.5 boundaries for China are produced and the boundaries define areas where the monthly PM_2.5 time series of any two cities show, on average, more than 50% similarity with each other. These areas demonstrate straightforwardly that PM_2.5 pollution is not limited to a single city or a single province. We also found that the PM_2.5 areas in China tend to be larger in cold months, but more fragmented in warm months, suggesting that, in cold months, the interactions between PM_2.5 concentrations in adjacent cities are stronger than in warmer months. The proposed framework provides a tool to delineate PM_2.5 boundaries and identify areas where PM_2.5 pollutants interact. It can help define air pollution management zones and assess impacts related to PM_2.5 pollution. It can also be used in analyses of other air pollutants.

Visualizing the intercity correlation of PM2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data

The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM_2.5) problem. To date, considerable progress has been made toward understanding the PM_2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM_2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM_2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM_2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM_2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM_2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM_2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM_2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM_2.5 pollution for impact analysis.

Lung cancer mortality clusters in Shandong Province, China: how do they change over 40 years?

Lung cancer has long been a major health problem in China. This study aimed to examine the temporal trend and spatial pattern of lung cancer mortality in Shandong Province from 1970 to 2013. Lung cancer mortality data were obtained from Shandong Death Registration System and three nationwide retrospective cause-of-death surveys. A Purely Spatial Scan Statistics method with Discrete Poisson models was used to detect possible high-risk spatial clusters. The results show that lung cancer mortality rate in Shandong Province increased markedly from 1970-1974 (7.22 per 100,000 person-years) to 2011-2013 (56.37/100, 000). This increase was associated with both demographic and non-demographic factors. Several significant spatial clusters with high lung cancer mortality were identified. The most likely cluster was located in the northern region of Shandong Province during both 1970-1974 and 2011-2013. It appears the spatial pattern remained largely consistent over the last 40 years despite the absolute increase in the mortality rates. These findings will help develop intervention strategies to reduce lung cancer mortality in this large Chinese population.

Causative impact of air pollution on evapotranspiration in the North China Plain

Atmospheric dispersion conditions strongly impact air pollution under identical surface emissions. The degree of air pollution in the Jing-Jin-Ji region is so severe that it may impose feedback on local climate. Reference evapotranspiration (ET₀) plays a significant role in the estimation of crop water requirements, as well as in studies on climate variation and change. Since the traditional correlation analysis cannot capture the causality, we apply the convergent cross mapping method (CCM) in this study to observationally investigate whether the air pollution impacts ET₀. The results indicate that southwest regions of Jing-Jin-Ji always suffer higher PM_2.5 concentration than north regions through the whole year, and correlation analysis suggests that PM_2.5 concentration has a significant negative effect on ET₀ in most cities. The causality detection with CCM quantitatively demonstrates the significantly causative influence of PM_2.5 concentration on ET₀, higher PM_2.5 concentration decreasing ET₀. However, CCM analysis suggests that PM_2.5 concentration has a relatively weak causal influence on ET₀ while the correlation analysis gives the near zero correlation coefficient in Zhangjiakou city, indicating that the causative influence of PM_2.5 concentration on ET₀ is better revealed with CCM method than the correlation analysis. Considering that ET₀ is strongly associated with crop water requirement, the amount of water for agricultural irrigation could be reduced at high PM_2.5 concentrations. These findings can be utilized to improve the efficiency of water resources utilization, and reduce the exploiting amount of groundwater in the Jing-Jin-Ji region, although PM_2.5 is detrimental to human health.

Constrained Mixed-Effect Models with Ensemble Learning for Prediction of Nitrogen Oxides Concentrations at High Spatiotemporal Resolution

Spatiotemporal models to estimate ambient exposures at high spatiotemporal resolutions are crucial in large-scale air pollution epidemiological studies that follow participants over extended periods. Previous models typically rely on central-site monitoring data and/or covered short periods, limiting their applications to long-term cohort studies. Here we developed a spatiotemporal model that can reliably predict nitrogen oxide concentrations with a high spatiotemporal resolution over a long time span (>20 years). Leveraging the spatially extensive highly clustered exposure data from short-term measurement campaigns across 1-2 years and long-term central site monitoring in 1992-2013, we developed an integrated mixed-effect model with uncertainty estimates. Our statistical model incorporated nonlinear and spatial effects to reduce bias. Identified important predictors included temporal basis predictors, traffic indicators, population density, and subcounty-level mean pollutant concentrations. Substantial spatial autocorrelation (11-13%) was observed between neighboring communities. Ensemble learning and constrained optimization were used to enhance reliability of estimation over a large metropolitan area and a long period. The ensemble predictions of biweekly concentrations resulted in an R2 of 0.85 (RMSE: 4.7 ppb) for NO2 and 0.86 (RMSE: 13.4 ppb) for NOx. Ensemble learning and constrained optimization generated stable time series, which notably improved the results compared with those from initial mixed-effects models.

An Ensemble Spatiotemporal Model for Predicting PM2.5 Concentrations

Although fine particulate matter with a diameter of <2.5 μm (PM2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 μm (PM10), measurements of PM2.5 have only recently been performed, and the spatial coverage of these measurements is limited. Comprehensively assessing PM2.5 pollution levels and the cumulative health effects is difficult because PM2.5 monitoring data for prior time periods and certain regions are not available. In this paper, we propose a promising approach for robustly predicting PM2.5 concentrations. In our approach, a generalized additive model is first used to quantify the non-linear associations between predictors and PM2.5, the bagging method is used to sample the dataset and train different models to reduce the bias in prediction, and the variogram for the daily residuals of the ensemble predictions is then simulated to improve our predictions. Shandong Province, China, is the study region, and data from 96 monitoring stations were included. To train and validate the models, we used PM2.5 measurement data from 2014 with other predictors, including PM10 data, meteorological parameters, remote sensing data, and land-use data. The validation results revealed that the R² value was improved and reached 0.89 when PM10 was used as a predictor and a kriging interpolation was performed for the residuals. However, when PM10 was not used as a predictor, our method still achieved a CV R² value of up to 0.86. The ensemble of spatial characteristics of relevant factors explained approximately 32% of the variance and improved the PM2.5 predictions. The spatiotemporal modeling approach to estimating PM2.5 concentrations presented in this paper has important implications for assessing PM2.5 exposure and its cumulative health effects.

Characteristics and source distribution of air pollution in winter in Qingdao, eastern China

To characterize air pollution and determine its source distribution in Qingdao, Shandong Province, we analyzed hourly national air quality monitoring network data of normal pollutants at nine sites from 1 November 2015 to 31 January 2016. The average hourly concentrations of particulate matter <2.5 μm (PM_2.5) and <10 μm (PM₁₀), SO₂, NO₂, 8-h O₃, and CO in Qingdao were 83, 129, 39, 41, and 41 μg m^-3, and 1.243 mg m^-3, respectively. During the polluted period, 19-26 December 2015, 29 December 2015 to 4 January 2016, and 14-17 January 2016, the mean 24-h PM_2.5 concentration was 168 μg m^-3 with maximum of 311 μg m^-3. PM_2.5 was the main pollutant to contribute to the pollution during the above time. Heavier pollution and higher contributions of secondary formation to PM_2.5 concentration were observed in December and January. Pollution pathways and source distribution were investigated using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses. A cluster from the west, originating in Shanxi, southern Hebei, and west Shandong Provinces, accounted for 44.1% of the total air masses, had a mean PM_2.5 concentration of 134.9 μg m^-3 and 73.9% trajectories polluted. This area contributed the most to PM_2.5 and PM₁₀ levels, >160 and 300 μg m^-3, respectively. In addition, primary crustal aerosols from desert of Inner Mongolia, and coarse and fine marine aerosols from the Yellow Sea contributed to ambient PM. The ambient pollutant concentrations in Qingdao in winter could be attributed to local primary emissions (e.g., coal combustion, vehicular, domestic and industrial emissions), secondary formation, and long distance transmission of emissions.

Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region

Due to complicated interactions in the atmospheric environment, quantifying the influence of individual meteorological factors on local PM_2.5 concentration remains challenging. The Beijing-Tianjin-Hebei (short for Jing-Jin-Ji) region is infamous for its serious air pollution. To improve regional air quality, characteristics and meteorological driving forces for PM_2.5 concentration should be better understood. This research examined seasonal variations of PM_2.5 concentration within the Jing-Jin-Ji region and extracted meteorological factors strongly correlated with local PM_2.5 concentration. Following this, a convergent cross mapping (CCM) method was employed to quantify the causality influence of individual meteorological factors on PM_2.5 concentration. The results proved that the CCM method was more likely to detect mirage correlations and reveal quantitative influences of individual meteorological factors on PM_2.5 concentration. For the Jing-Jin-Ji region, the higher PM_2.5 concentration, the stronger influences meteorological factors exert on PM_2.5 concentration. Furthermore, this research suggests that individual meteorological factors can influence local PM_2.5 concentration indirectly by interacting with other meteorological factors. Due to the significant influence of local meteorology on PM_2.5 concentration, more emphasis should be given on employing meteorological means for improving local air quality.