Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution

Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.

Roles of ambient temperature and relative humidity on the relationship between fine particulate matter and gaseous pollutants in the largest industrial city of Ulsan, South Korea

Although meteorological conditions play a significant role in air pollution, research on their effects on the relationship between air pollutants is limited. In this study, trends of six criteria air pollutants were investigated from 15 air quality monitoring stations (AQMSs) in Ulsan, a multi-industrial city in South Korea, during 2015-2019. Unlike CO and O3, SO2, NO2, PM10, and PM2.5 showed statistically significant decreasing trends over the period. The companion relationship between PM2.5 and gaseous pollutants was evaluated by their correlations [R (PM2.5-GPs)]. R (PM2.5-NO2) was relatively high at almost all AQMSs, whereas high R (PM2.5-SO2) was observed near the petrochemical industrial complex, suggesting a great influence of local emissions (vehicles and industries). R (PM2.5-CO) and the standardized regression coefficients of CO obtained from the multiple linear regression model were the highest, indicating that combustion processes may significantly contribute to PM2.5. The effect of temperature (T) was more apparent on R (PM2.5-GPs) than that of relative humidity, with significant values under T > 15 °C. Moreover, R (PM2.5-O3) was positive at the T range of 12-18 °C, suggesting that reducing GPs emitted by industrial facilities during May-June may control PM2.5 and O3 in Ulsan. The methodology demonstrated in this study can be further used for a better understanding of the influences of environmental factors on the secondary PM2.5 formation from gaseous precursors and the R (PM2.5-O3).

Assessment of eutrophication from Xiaoqing River estuary to Laizhou Bay: Further warning of ecosystem degradation in typically polluted estuary

The coast of Laizhou Bay is plagued by a number of environmental issues, such as eutrophication, which are likely to worsen over the next few decades as a result of trends toward industrialization and urbanization. High nutrient levels in the Xiaoqing River are believed to be the main cause of Laizhou Bay becoming eutrophicated. Therefore, we conducted two cruises from the Xiaoqing River estuary to Laizhou Bay in August 2022 and December 2022, respectively, in the wet and dry periods to assess the potential impact of status of eutrophication due to human activities. The results showed that the concentration of DIN was higher than the quality standard for water (f_i > 1) in both the wet season (August 2022) and the dry season (December 2022). DIN has major environmental impacts in Laizhou Bay. The eutrophication level index and organic pollution index have obvious spatial and temporal characteristics. In terms of time, the dry season is higher than the wet season. In space, Xiaoqing estuary is higher than Laizhou Bay. In the two surveys, DIN and DIP concentrations were significantly positively correlated, indicating that N and P pollution in the region had a common source and destination, and the spatial distribution was also similar. In addition, the current environmental conditions in the region are not ideal, reaching moderate and severe eutrophication levels, which proves that the ecosystem has the risk of aggravating degradation. As the Xiaoqing River is about to resume full navigation, human-related nutrient input (especially DIN) will continue to increase, and it is expected that the eutrophication risk in this area will increase in the next few years due to the increase in nutrient load.

Increased diurnal difference of NO2 concentrations and its impact on recent ozone pollution in eastern China in summer

Nitrogen dioxide (NO₂) is a key tropospheric O₃ precursor. Since 2013, efforts to decrease air pollution in China have driven substantial declines in annual NO₂ concentrations, whereas ozone (O₃) concentrations have increased. Based on nationwide NO₂ observations and a regional air quality model (WRF-CMAQ), we analyzed trends in the diurnal difference (DD, the difference between nighttime and daytime concentrations) of NO₂ concentrations across eastern China and in five national urban agglomerations (UAs) from 2014 to 2021, and explored the factors underlying such changes and the potential impacts on O₃ pollution. We found that the observed DD of NO₂ has increased in most cities and UAs, and that this trend can be primarily attributed to changes in anthropogenic emissions, based on comparison with DDs simulated with fixed anthropogenic emissions, which generally showed much weaker trends and little interannual variation. A sensitivity analysis using the WRF-CMAQ model was conducted to investigate the impact of a modified diurnal cycle of nitrogen oxides (NO_x) emissions on O₃ concentrations. The result revealed that enhancing the DD of NO₂ would increase O₃ concentrations in the morning and the daily maximum 8-h O₃ concentrations in the cities with high NO_x concentrations, as well as downwind areas of cities, indicating that greater DDs in NO₂ is one of the reasons that have led to the enhanced China's O₃ pollution in recent years.

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO₂, and SO₂). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO₂ is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO₂ concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO₂ concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

Sulphur dioxide and fluoride co-exposure induce incisor hypomineralization and amelogenin upregulation via YAP/RUNX2 signaling pathway

Sulphur dioxide (SO₂) and fluoride are among the most common environmental pollutants affecting human health, and both co-exist in areas predominantly consuming coal. It is vital to analyse the combined toxicity of SO₂ and fluoride, and their effects on health and the underlying mechanisms of their co-exposure have not yet been adequately assessed. In the present study, we used ICR mice and LS8 cells to investigate the toxicity of SO₂ and fluoride exposure to the enamel, alone or in combination. Factorial design analysis was used to reveal the combined toxicity in vitro and in vivo. Co-exposure to SO₂ and fluoride exacerbated enamel injury, resulting in more severe hypomineralization of incisor, and enamel structure disorders in mice, and could induce the accumulation of protein residue in the matrix of the enamel. Amelogenin expression was increased upon exposure to SO₂ and fluoride, but enamel matrix proteases were not affected. Consistent with our in vivo results, co-exposure of SO₂ and fluoride aggravated amelogenin expression in LS8 cells, and increased the YAP and RUNX2 levels. Co-exposure to SO₂ and fluoride resulted in greater toxicity than individual exposure, both in vitro and in vivo, indicating that residents of areas exposed to SO₂ and fluoride may have an increased risk of developing enamel damage.

Impact of particle sizes on health risks and source-specific health risks for heavy metals in road dust

To analyze the impact of particle sizes on sources and related health risks for heavy metals, road dust samples in Beijing were collected and sifted into five particle sizes. The positive matrix factorization (PMF), human health risk assessment model (HHRA), and Monte Carlo simulation were used in the health risk assessment and source apportionment. Results showed that mass of particles < 74 μm occupied about 50% of the total particles, while only 8.48% of the particles were > 500 μm. Mass distribution and concentrations of heavy metals in each particle size changed in temporal. Over 85.00% of carcinogenic risks (CR) were from particles <74 μm, whereas CR from particles >250 μm were ignorable. Sources for health risks in each particle size were traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Proportions of sources to CR differed among particle sizes. Traffic exhaust and fuel combustion contributed over 90% to CR in particles <74 μm, whereas construction contributed the highest (31.68-54.14%) among all sources in particles 74-250 μm. Furthermore, the difference between health risks based on sifted road dust and that based on unsifted road dust was quantitatively analyzed. Source-specific health risk apportionment based on unsifted road dust was not presentative to all particle sizes, and true value of health risks could be over 2.5 times of the estimated value based on unsifted road dust, emphasized the importance of sifting of road dust.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM_2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM_2.5 pollution in the megacities clustered in eastern China, the behavior of PM_2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015-2019 obtained from the national air environmental monitoring network to examine variation in PM_2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM_2.5 in the study region. The annual average PM_2.5 concentrations over the study period was 54.3 μg/m³, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320-370 d and 150-200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO₂, NO₂, relative humidity, sunshine duration and temperature. The two most primary variables, NO₂ and SO₂, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO₂ and other factors had the strongest effect on PM_2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM_2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

Spatiotemporal Variation in Ground Level Ozone and Its Driving Factors: A Comparative Study of Coastal and Inland Cities in Eastern China

Variations in marine and terrestrial geographical environments can cause considerable differences in meteorological conditions, economic features, and population density (PD) levels between coastal and inland cities, which in turn can affect the urban air quality. In this study, a five-year (2016-2020) dataset encompassing air monitoring (from the China National Environmental Monitoring Centre), socioeconomic statistical (from the Shandong Province Bureau of Statistics) and meteorological data (from the U.S. National Centers for Environmental Information, National Oceanic and Atmospheric Administration) was employed to investigate the spatiotemporal distribution characteristics and underlying drivers of urban ozone (O₃) in Shandong Province, a region with both land and sea environments in eastern China. The main research methods included the multiscale geographically weighted regression (MGWR) model and wavelet analysis. From 2016 to 2019, the O₃ concentration increased year by year in most cities, but in 2020, the O₃ concentration in all cities decreased. O₃ concentration exhibited obvious regional differences, with higher levels in inland areas and lower levels in eastern coastal areas. The MGWR analysis results indicated the relationship between PD, urbanization rate (UR), and O₃ was greater in coastal cities than that in the inland cities. Furthermore, the wavelet coherence (WTC) analysis results indicated that the daily maximum temperature was the most important factor influencing the O₃ concentration. Compared with NO, NO₂, and NO_x (NO_x ≡ NO + NO₂), the ratio of NO₂/NO was more coherent with O₃. In addition, the temperature, the wind speed, nitrogen oxides, and fine particulate matter (PM_2.5) exerted a greater impact on O₃ in coastal cities than that in inland cities. In summary, the effects of the various abovementioned factors on O₃ differed between coastal cities and inland cities. The present study could provide a scientific basis for targeted O₃ pollution control in coastal and inland cities.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O₃, NO₂, SO₂, PM_2.5 and PM₁₀ of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM_2.5, PM₁₀ and CO are the main pollutants affecting air quality and fluctuation, followed by SO₂, NO₂ and O₃.

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.

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

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

Spatio-temporal analysis of urban air pollutants throughout China during 2014-2019

Air pollution control has become the top priority of China's "green development" concept since 2013. The Chinese government has enacted a range of policies and statutes to control contaminant emissions and improve air quality. On the basis of the national air quality ground observation database, the spatial and temporal distribution of air quality index value (AQI), fine particulate matter (PM_2.5), coarse particles (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) were explored in 336 cities throughout China from 2014 to 2019. AQI and most pollutants (except O₃) decreased in concentrations from 2014 to 2019. In 2019, all cities except Henan reached the level 2 of the ambient air quality index, and six cities had a lower ambient air quality index and reached the level 1. Spatially, higher pollutant concentrations were concentrated in large city clusters, whereas the areas with high O₃ concentration were found across the country. Furthermore, central heating was shown to have a negative impact on air quality. The observed AQI value, PM_2.5, PM₁₀, SO₂, NO₂, and CO concentrations were highest in north and northwest China and Henan province in central China. The correlations among pollutants suggest that the main sources of pollutants are fossil fuel combustion, industrial production, and motor vehicle emissions. The influence of meteorological factors on air quality, long-distance transportation, and the transformations of pollutants should be explored in future research.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01043-5.

Particle size distributions and health risks of polychlorinated dibenzo-p-dioxin/furans, polychlorinated biphenyls, and polychlorinated naphthalenes in atmospheric particles around two secondary copper smelters in Shandong Province, China

Sixteen samples of atmospheric particles in four size fractions (diameter: > 10 μm, 5-10 μm, 2.5-5 μm, and <2.5 μm) were collected around two secondary copper smelters in Shandong Province, China. The levels, particle size distributions, and potential health risks of polychlorinated dibenzo-p-dioxin/furans (PCDD/Fs), dioxin-like polychlorinated biphenyls (dl-PCBs), and polychlorinated naphthalenes (PCNs) in these samples were analyzed. The concentration ranges for the PCDD/Fs, dl-PCBs, and PCNs were 3.13-5.77 pg m^-3, 0.43-0.56 pg m^-3, and 4.76-9.89 pg m^-3, respectively. All of the compounds accumulated strongly in the particles with diameters of <2.5 μm. The congener profiles of PCDD/Fs in particles of various sizes were similar, and were consistent with those in stack gases from local secondary copper smelters. However, the congener profiles of dl-PCBs and PCNs in the particles with diameters of <2.5 μm differed from those for the other particle size fractions. The proportion of highly chlorinated homologs in particles with diameters of <2.5 μm was much higher than that of particles with diameters of >2.5 μm. The results of a risk assessment indicated that the contribution of PCDD/Fs to the total carcinogenic risk (PCDD/Fs + dl-PCBs + PCNs) was >95%. For the PCDD/Fs, dl-PCBs, and PCNs, 78%, 71%, and 86% of the carcinogenic risk was associated with the <2.5 μm fraction, respectively. This study improves our understanding of the particle size distributions and human health risks of exposure to PCDD/Fs, dl-PCBs, and PCNs in the atmosphere around secondary copper smelters.

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

At present, China’s air pollution and its treatment effect are issues of general concern in the academic circles. Based on the analysis of the development stages of air pollution in China and the development history of China’s air quality standards, we selected 17 cities of Shandong Province, China as the research objects. By expanding China’s existing Air Quality Index System, the air quality of six major pollutants including PM_2.5 and PM₁₀ in 17 cities from February 2017 to January 2020 is comprehensively evaluated. Then, with a forecast model, the air quality of the above cities in the absence of air pollution control policies since June 2018 was simulated. The results of the error test show that the model has a maximum error of 4.67% when simulating monthly assessment scores, and the maximum mean error of the four months is 3.17%. Through the comparison between the simulation results and the real evaluation results of air quality, we found that since June 2018, the air pollution control policies of six cities have achieved more than 10% improvement, while the air quality of the other 11 cities declined. The different characteristics of pollutants and the implementation of governance policies are perhaps the main reasons for the above differences. Finally, policy recommendations for the future air pollution control in Shandong and China were provided.

Spatial variation in biodiversity loss across China under multiple environmental stressors

Biodiversity is essential for the maintenance of ecosystem health and delivery of the Sustainable Development Goals. However, the drivers of biodiversity loss and the spatial variation in their impacts are poorly understood. Here, we explore the spatial-temporal distributions of threatened and declining ("biodiversity-loss") species and find that these species are affected by multiple stressors, with climate and human activities being the fundamental shaping forces. There has been large spatial variation in the distribution of threatened species over China's provinces, with the biodiversity of Gansu, Guangdong, Hainan, and Shaanxi provinces severely reduced. With increasing urbanization and industrialization, the expansion of construction and worsening pollution has led to habitat retreat or degradation, and high proportions of amphibians, mammals, and reptiles are threatened. Because distributions of species and stressors vary widely across different climate zones and geographical areas, specific policies and measures are needed for preventing biodiversity loss in different regions.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

PM_2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM_2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM_2.5 from 2015 to 2018 in the YRDUA. Results showed that PM_2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM_2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM_2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM_2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM_2.5 and CO was the most consistent, and the correlation between PM_2.5 and SO₂/NO₂ improved with the increase of time scale, while the correlation between PM_2.5 and O₃ was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM_2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

Spatiotemporal disequilibrium and spillover effect of fine particulate matter across China

Massive monitoring data requires effective statistical analysis methods. This paper aims to visualize the spatiotemporal characteristics and spillover effect of air pollutants. Ground-based PM_2.5 data in 336 cities across China revealed a tough but improving situation. The PM_2.5 average annual concentrations in 2016 and 2017 were 47 ± 18 µg/m³ and 44 ± 16 µg/m³ respectively, but a worse, or at least a not-improving PM_2.5 situation happened in winter. A slight declining north-south disequilibrium and a growing east-west disequilibrium exhibited in 2017, along with an increasing weight of eastern and southern pollution in the proportion of the overall pollution level. North-south disequilibrium existed stably throughout the year but east-west disequilibrium was erratic. Nearly half of the cities exhibited significant spillover effects, presenting 2 clusters with spillover by high concentrations and 3 clusters with spillover by low concentrations. Most cities in Hebei, Shandong and Henan provinces showed a high but decreasing spillover effect, but increasing trend happened in the cities in Anhui and Shanxi provinces. A significant correlation appeared between the city population and PM_2.5 concentration. Population density explained about 25% of the PM_2.5 concentration change, and the explanation ability increased in 2017. A higher influence of population on PM_2.5 concentration happened in the early stage of city development, and the influence exhibited spatial differences. The city population and PM_2.5 spillover effect existed an overall positive correlation, but the population only addressed about 10% of the spillover effect change. Our findings provide important information for the joint prevention and control of air pollution in China, and the approach proposed in this paper is applicable to other fields.

Short-term association between ambient air pollution and lung cancer mortality

RATIONALE

Long-term exposure to air pollution has been associated with increased lung cancer incidence and mortality. However, the short-term association between air pollution and lung cancer mortality (LCM) remains largely unknown.

METHODS

We collected daily data on particulate matter with diameter <2.5 μm (PM_2.5), particulate matter with diameter < 10 μm (PM₁₀), sulfur dioxide (SO₂), and ozone (O₃), and LCM in three of the biggest cities in China, i.e. Beijing, Chongqing, and Guangzhou, from 2013 to 2015. We first estimated city-specific relationships between air pollutants and LCM using time-series generalized linear models, adjusting for potential confounders. A classification and regression tree (CART) model was used to stratify LCM risk based on combinations of air pollutants and meteorological factors in each city. Then we pooled the city-specific associations using random-effects meta-analysis. Meta regression was used to explore if city-specific characteristics modified the air pollution-LCM association. Finally, we stratified the analyses by season, age, and sex.

RESULTS

Over the entire period, the current-day concentrations of PM_2.5 and PM₁₀ in Chongqing and PM_2.5, PM₁₀, and SO₂ in Guangzhou were positively associated with LCM (Excess risk ranged from 0.72% (95% CI 0.27%-1.17%) to 6.06% (95% CI 0.76%-11.64%) with each 10 μg/m³ increment in different pollutants), but the association between current-day air pollution and LCM in Beijing was not significant (P > 0.05). When considering the environmental and weather factors simultaneously, current-day PM_2.5, relative humidity, and PM₁₀ were the most important factors associated with LCM in Beijing, Chongqing, and Guangzhou, respectively. LCM risk related with daily PM_2.5, PM₁₀, and SO₂ significantly increased with the increasing annual mean temperature and humidity of the city, while LCM risk related with daily O₃ significantly increased with the increases of latitude, annual mean O₃ concentration, and socioeconomic level. After stratification, the current-day PM_2.5, PM₁₀, and O₃ during the warm season in Beijing and PM_2.5, PM₁₀, and SO₂ during the cool season in Chongqing and Guangzhou were positively associated with LCM (Excess risk ranged from 0.93% (95% CI 0.42%-1.45%) to 7.16% (95% CI 0.64%-14.09%) with each 10 μg/m³ increment in different pollutants). Male and the elderly lung cancer patients were more sensitive to the short-term effect of air pollution.

CONCLUSIONS

Lung cancer patients should enhance protection measures against air pollution. More attentions should be paid for the high PM_2.5, PM₁₀, and O₃ during the warm season in Beijing, and high PM_2.5, PM₁₀, and SO₂ during the cool season in Chongqing and Guangzhou.

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.