Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis

A systematic review of spatial and temporal epidemiological approaches, focus on lung cancer risk associated with particulate matter

BACKGROUND

Particulate matter (PM), including the major risk factor for lung cancer (LC), greatly impacts human health. Although numerous studies have highlighted spatiotemporal patterns and PM-LC associations, these studies have not been well-reviewed. Thus, we examined epidemiological studies linked with PM-LC and provided concise, up-to-date data.

METHODS

We used certain keywords to review articles published in PubMed, Web of Science, Scopus, and Google Scholar until 30th June 2024 and identified 1474 research articles. We then filtered the research articles based on our criteria and ultimately dropped down to 30 for this review.

RESULTS

Out of the thirty reviewed studies on the PM-LC relation, twenty-four focused on PM2.5, four on PM10, and two on both, indicating that approximately 80% of the respondents were inclined toward fine particles and their health impacts. The study revealed that 22 studies used visualization, 12 used exploration, and 15 used modeling methods. A strong positive relationship was reported between LC and PM2.5, ranging from 1.04 to 1.60 (95% CI) for a 10 µg/m3 increase in PM2.5 exposure. However, compared to PM2.5, PM10 was found to have a significantly less positive association.

CONCLUSIONS

Very few studies have used advanced spatiotemporal methods to examine the association between LC and PM. Advanced spatiotemporal analysis techniques should be employed to explore this association in specific geographical locations. Further research should utilize spatiotemporal epidemiological approaches to study the link between PM and lung cancer.

Effect of forest cover on lung cancer incidence: a case study in Southwest China

Introduction

Forests are closely linked to human health, particularly about lung cancer incidence. However, there is currently limited research on how forest coverage and different types of forests influence lung cancer rates. This study aims to address this gap by examining how the coverage of various forest types impacts lung cancer incidence in Southwest China, thereby providing theoretical support for health-oriented forest structure planning.

Methods

We focused on 438 counties in Southwest China, employing spatial autocorrelation analysis (Moran's I) and spatial regression models [including Spatial Lag Model (SLM), Spatial Error Model (SEM), and Spatial Durbin Model (SDM)] to explore the effects of forest coverage and internal forest structure on lung cancer incidence. We used ArcGIS to visualize lung cancer incidence and forest coverage rates across the study area.

Results

The study found a significant negative correlation between forest coverage and lung cancer incidence. Specifically, for every 1% increase in forest coverage, lung cancer incidence decreased by 0.017 levels. Evergreen forests and mixed forests showed a significant negative impact on lung cancer rates, with evergreen forests having a particularly strong effect; a 1% increase in evergreen forest coverage was associated with a 0.027 level decrease in lung cancer incidence. In contrast, deciduous forests had no significant impact. Additionally, the study revealed a marked spatial heterogeneity in lung cancer incidence and forest coverage across Southwest China: higher lung cancer rates were observed in the eastern regions, while forest coverage was predominantly concentrated in the western and southern regions.

Discussion

This study demonstrates that increasing forest coverage, particularly of evergreen and mixed forests, can help reduce lung cancer incidence. This effect may be related to the ability of forests to absorb harmful gasses and particulate matter from the air. Furthermore, the spatial heterogeneity in lung cancer incidence suggests that regional economic development levels and urbanization processes may also play significant roles in the spatial distribution of lung cancer rates. The findings provide empirical support for the development of targeted forest conservation and development policies aimed at optimizing regional forest structures to reduce the risk of lung cancer.

Tire Wear Chemicals in the Urban Atmosphere: Significant Contributions of Tire Wear Particles to PM2.5

Tire wear particles (TWPs) containing tire wear chemicals (TWCs) are of global concern due to their large emissions and potential toxicity. However, TWP contributions to urban fine particles are poorly understood. Here, 72 paired gas-phase and PM2.5 samples were collected in the urban air of the Pearl River Delta, China. The concentrations of 54 compounds were determined, and 28 TWCs were detected with total concentrations of 3130-317,000 pg/m3. Most p-phenylenediamines (PPDs) were unstable in solvent, likely leading to their low detection rates. The TWCs were mainly (73 ± 26%) in the gas phase. 2-OH-benzothiazole contributed 82 ± 21% of the gas-phase TWCs and benzothiazole-2-sulfonic acid contributed 74 ± 18% of the TWCs in PM2.5. Guangzhou and Foshan were "hotspots" for atmospheric TWCs. Most TWC concentrations significantly correlated with the road length nearby. More particulate TWCs were observed than model predictions, probably due to the impacts of nonexchangeable portion and sampling artifacts. Source apportionment combined with characteristic molecular markers indicated that TWPs contributed 13 ± 7% of urban PM2.5. Our study demonstrates that TWPs are important contributors to urban air pollution that could pose risks to humans. There is an urgent need to develop strategies to decrease TWP emissions, along with broader urban air quality improvement strategies.

Influence of the atmospheric environment on spatial variation of lung cancer incidence in China

Conducting this research contributes to a deeper understanding of the correlation between atmospheric environmental quality and lung cancer incidence, and provides the scientific basis for formulating effective environmental protection and lung cancer prevention and control strategies. Lung cancer incidence in China has strong spatial variation. However, few studies have systematically revealed the characteristics of the spatial variation in lung cancer incidence, and have explained the causes of this spatial variation in lung cancer incidence from the perspectives of multiple components of the atmospheric environment to explain this spatial variation in lung cancer incidence. To address research limitations, we first analyze the spatial variation and spatial correlation characteristics of lung cancer incidence in China. Then, we build a spatial regression model using GeoDa software with lung cancer incidence as the dependent variable, five atmospheric environment factors-particulate matter 2.5 (PM2.5) concentration, temperature, atmospheric pressure, and elevation as explanatory variables, and four socio-economic characteristics as control variables to systematically analyze the influence and intensity of these factors on lung cancer incidence. The results show that lung cancer incidence in China has apparent changes in geographical and spatial unevenness, and spatial autocorrelation characteristics. In China, the lung cancer incidence is relatively high in Northeast China, while some areas of high lung cancer incidence still exist in Central China, Southwest China and South China, although the overall lung cancer incidence is relatively low. The atmospheric environment significantly affects lung cancer incidence. Different elements of the atmospheric environment vary in the direction and extent of their influence on the development of lung cancer. A 1% increase in PM2.5 concentration is associated with a level of 0.002975 increase in lung cancer incidence. Atmospheric pressure positively affects lung cancer incidence, and an increase in atmospheric pressure by 1% increases lung cancer incidence by a level of 0.026061. Conversely, a 1% increase in temperature is linked to a level of 0.006443 decreases in lung cancer incidence, and a negative correlation exists between elevation and lung cancer incidence, where an increase in elevation by 1% correlates with a decrease in lung cancer incidence by a level of 0.000934. The core influencing factors of lung cancer incidence in the seven geographical divisions of China exhibit variations. This study facilitates our understanding of the spatial variation characteristics of lung cancer incidence in China on a finer scale, while also offering a more diverse perspective on the impact of the atmospheric environment on lung cancer incidence.

Validation and calibration of aerosol optical depth and classification of aerosol types based on multi-source data over China

A refined classification of aerosol types is essential to identify and control air pollution sources. This study focused on improving the resolution and accuracy of aerosol optical depth (AOD) and further refining the classification of aerosol types in China. We validated the accuracy of the AOD acquired using the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) and Copernicus Atmosphere Monitoring Service (CAMS) by comparing it with that acquired using from the Aeronet Robotic Network (AERONET). We simulated the AOD with high spatial resolution and accuracy based on the extremely randomized trees (ERT), adaptive boosting (AdaBoost), and gradient boosting decision trees (GBDT) models and identified aerosol types based on the Angstrom Exponent (AE) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the calibrated AOD. The results showed that CAMS overestimates AOD (21.4 %) and MERRA2 underestimates AOD (-17.3 %). Among the three machine learning models, the ERT model performed best, with a determination coefficient (R2) of 0.825 and the root-mean-square error (RMSE) of 0.174. Biomass burning/urban-industrial aerosols dominated China, with the largest contributions to southern, eastern, and central China in spring and summer. Clean continental aerosols contributed the most to southwestern China in fall and winter, whereas desert dust aerosols contributed the most to northwestern and eastern China in spring.

China's air quality improvement strategy may already be having a positive effect: evidence based on health risk assessment

Aiming to investigate the health risk impact of PM2.5 pollution on a heavily populated province of China. The exposure response function was used to assess the health risk of PM2.5 pollution. Results shows that the total number of premature deaths and diseases related to PM2.5 pollution in Shandong might reach 159.8 thousand people based on the new WHO (2021) standards. The health effects of PM2.5 pollution were more severe in men than in women. Five of the 16 cities in Shandong had higher health risks caused by PM2.5 pollution, including LinYi, HeZe, JiNing, JiNan, and WeiFang. PM2.5 pollution resulted in nearly 7.4 billions dollars in healthy economic cost, which accounted for 0.57% of GDP in Shandong in 2021. HeZe, LiaoCheng, ZaoZhuang, and LinYi were the cities where the health economic loss was more than 1% of the local GDP, accounted for 1.30, 1.26, 1.08, and 1.04%. Although the more rigorous assessment criteria, the baseline concentration was lowered by 30 μg/m3 compared to our previous study, there was no significant increase in health risks and economic losses. China's air quality improvement strategy may already be having a positive effect.

Identifying the Environmental Determinants of Lung Cancer: A Case Study of Henan, China

Lung cancer has become one of the most prevalent cancers in the last several decades. Studies have documented that most cases of lung cancer are caused by inhaling environmental carcinogens while how external environmental factors lead to individual lung cancer is still an open issue as the pathogenesis may come from the combined action of multiple environmental factors, and such pathogenic mechanism may vary from region to region. Based on the data of lung cancer cases from hospitals at the county level in Henan from 2016 to 2020, we analyzed the response relationship between lung cancer incidence and physical ambient factors (air quality, meteorological conditions, soil vegetation) and socioeconomic factors (occupational environment, medical level, heating mode, smoking behavior). We used a Bayesian spatio-temporal interaction model to evaluate the relative risk of disease in different regions. The results showed that smoking was still the primary determinant of lung cancer, but the influence of air quality was increasing year by year, with meteorological conditions and occupational environment playing a synergistic role in this process. The high-risk areas were concentrated in the plains of East and Central Henan and the basin of South Henan, while the low-risk areas were concentrated in the hilly areas of North and West Henan, which were related to the topography of Henan. Our study provides a better understanding of the environmental determinants of lung cancer which will help refine existing prevention strategies and recognize the areas where actions are required to prevent environment and occupation related lung cancer.

Public Concern about Haze and Ozone in the Era of Their Coordinated Control in China

In China, due to the implementation of the Action Plan for Prevention and Control of Air Pollution (APPCAP), the concentrations of PM_2.5 (fine particulate matter) and severe haze in most cities have decreased significantly. However, at present, haze pollution in China has not been completely mitigated, and the problem of O₃ (ozone) has become prominent. Therefore, the prevention and control of haze and O₃ pollution have become important and noticeable issues in the field of atmospheric management. We used the Baidu search indices of "haze" and "ozone" to reflect public concerns about air quality and uncover different correlations between level of concern and level of pollution, and then we identified regions in China that require public attention. The results showed that (1) over the last decade, the search index of haze had a rapid trend of variation in line with changes in haze pollution, but that of O₃ had a relatively slowly increasing trend; (2) the lag days between the peaks of public concern and the peaks of air pollution became increasingly shorter according to daily data analysis; and (3) 96 polluted cities did not receive sufficient public attention. Although periods of heavily haze-polluted weather, which affects visibility, have generated much public concern, periods of slight pollution have not received enough public attention. Public health protection and environmental participation regarding these periods of slight pollution in China deserve appropriate levels of attention.

Air quality and health benefits of increasing carbon mitigation tech-innovation in China

Most studies on the short-term local benefits of carbon mitigation technologies on air quality improvement and health focus on specific technologies such as biofuels or carbon sequestration technologies, while ignoring the overall role of the growing scale of low-carbon technologies. Based on STIRPAT model and EKC hypothesis, this paper takes 30 provinces in China from 2004 to 2016 as research samples. We builded the panel double fixed effect model to empirical analysis of climate change on carbon mitigation tech-innovation suppressing the influence of haze pollution, on this basis, the mediating effect model was used to explore the mediation function of industrial structure and energy structure. Meanwhile, we drawed on the existing studies on air quality and health benefits, and quantify the co-benefits of carbon mitigation tech-innovation on health through the equivalent substitution formula. It shows that a 1% increase in the number of low-carbon patent applications can reduce haze pollution by 0.066%. According to this estimate, to 2029, China's carbon mitigation tech-innovation could reduce PM2.5 concentration to 15 μg/m³ preventing 5.597 million premature deaths. Moreover, carbon mitigation tech-innovation can also indirectly inhibit haze pollution by triggering more systematic economic structure changes such as energy and industrial structure. Additionally, we found that the role of gray tech-innovation (GT) related to improving the efficiency of fossil energy is stronger than that of clean technology (CT) related to the use of renewable energy. This suggests that for a large economy such as China, where coal is still the dominant source of energy consumption, the short-term local benefits of improving air quality and health through the use of gray tech-innovation to improve energy and industrial structure are still important to balance the cost of carbon mitigation.

Morphology and Dust-Suppression Evaluation of Fugitive Dust Particles in Beijing

Fugitive dust particles are important contributors to urban ambient particulate matter (PM), while their emissions have been ignored or greatly underestimated in previous studies, leading to the underestimation of PM concentrations and health impacts. Thus, studying the morphology of fugitive dust, taking appropriate dust-suppression measures, and evaluating dust-suppression effects are crucial to the prevention and control of fugitive dust. In this study, we investigated the morphology and composition of dust particles from different dust sources, including bare land, stock dump, construction, and road dust. Afterwards, different dust-suppression measures including fence interception nets, bare ground mesh nets, and road dust-suppressants were undertaken to simulate and analyze their dust-suppression effects. Finally, the height concentration profiling method was used to comprehensively evaluate the on-site dust-suppression effect, which can not only accurately evaluate the dust-suppression effect, but also predict the dust-suppression ability in a wide range. Gaining insights into the morphology and composition of dust from representative sources is an important step forward to prevent and control fugitive dust, and selecting an appropriate dust-suppression effect evaluation method will provide a beneficial guide for effectively controlling PM pollution in the future.

Research on physical health early warning based on GM(1,1)

At present, hundreds of millions of Chinese people face increasingly serious health risks, and health checks have undoubtedly played a significant role in finding health risks. However, the current health check in China mainly judges the quality of physical functions by a single index value without dynamic analysis of the changing trends of the index, which may lead to unreasonable diagnostic conclusions. In this paper, the data characteristics of physical indicators are systematically analyzed, and grey system models dedicated to data with the characteristics are applied to simulate and predict the changing trends of body indicators. On this basis, possible pathological changes in body organs were identified. Specifically, this paper analyses the state of human kidney functions by grey prediction models. The results showed that even when the renal function index (serum creatinine) is within the normal range, the human renal function might be abnormal. The grey model analysis of the change trends of serum creatinine can predict the potential health hazards of renal functions.

Using Multisource Data to Assess PM2.5 Exposure and Spatial Analysis of Lung Cancer in Guangzhou, China

Elevated air pollution, along with rapid urbanization, have imposed higher health risks and a higher disease burden on urban residents. To accurately assess the increasing exposure risk and the spatial association between PM_2.5 and lung cancer incidence, this study integrated PM_2.5 data from the National Air Quality Monitoring Platform and location-based service (LBS) data to introduce an improved PM_2.5 exposure model for high-precision spatial assessment of Guangzhou, China. In this context, the spatial autocorrelation method was used to evaluate the spatial correlation between lung cancer incidence and PM_2.5. The results showed that people in densely populated areas suffered from higher exposure risk, and the spatial distribution of population exposure risk was highly consistent with the dynamic distribution of the population. In addition, areas with PM_2.5 roughly overlapped with areas with high lung cancer incidence, and the lung cancer incidence in different locations was not randomly distributed, confirming that lung cancer incidence was significantly associated with PM_2.5 exposure. Therefore, dynamic population distribution has a great impact on the accurate assessment of environmental exposure and health burden, and it is necessary to use LBS data to improve the exposure assessment model. More mitigation controls are needed in highly populated and highly polluted areas.

Retrieval of Fine-Grained PM2.5 Spatiotemporal Resolution Based on Multiple Machine Learning Models

Due to the country’s rapid economic growth, the problem of air pollution in China is becoming increasingly serious. In order to achieve a win-win situation for the environment and urban development, the government has issued many policies to strengthen environmental protection. PM2.5 is the primary particulate matter in air pollution, so an accurate estimation of PM2.5 distribution is of great significance. Although previous studies have attempted to retrieve PM2.5 using geostatistical or aerosol remote sensing retrieval methods, the current rough resolution and accuracy remain as limitations of such methods. This paper proposes a fine-grained spatiotemporal PM2.5 retrieval method that comprehensively considers various datasets, such as Landsat 8 satellite images, ground monitoring station data, and socio-economic data, to explore the applicability of different machine learning algorithms in PM2.5 retrieval. Six typical algorithms were used to train the multi-dimensional elements in a series of experiments. The characteristics of retrieval accuracy in different scenarios were clarified mainly according to the validation index, R2. The random forest algorithm was shown to have the best numerical and PM2.5-based air-quality-category accuracy, with a cross-validated R2 of 0.86 and a category retrieval accuracy of 0.83, while both maintained excellent retrieval accuracy and achieved a high spatiotemporal resolution. Based on this retrieval model, we evaluated the PM2.5 distribution characteristics and hourly variation in the sample area, as well as the functions of different input variables in the model. The PM2.5 retrieval method proposed in this paper provides a new model for fine-grained PM2.5 concentration estimation to determine the distribution laws of air pollutants and thereby specify more effective measures to realize the high-quality development of the city.

The impact of environmental pollution on the physical health of middle-aged and older adults in China

While several studies have demonstrated the negative impacts of environmental pollution on population health, in general, few studies have examined the potential differential effects on the physical health of middle-aged and older populations, i.e., 45 years and older. Given the twin concerns of environmental pollution and population aging in China, this article employed a fixed effects model to infer the impact of environmental pollution on public health with a particular focus on middle-aged and older adults. The analyses were based on data from the 2011 to 2018 waves of the CHARLS and pollutant data from prefecture-level cities. The results showed that both the level and intensity of environmental pollution significantly increased the risk of chronic diseases and negatively impacted the physical health of middle-aged and older adults. Environmental pollution had its greatest negative effect on the physical health of the elderly, urban residents, residents of the Eastern region, and those with lower incomes than their counterparts. We further found that the potential channels of health effect were through reduced physical exercise and sleep duration and an increase in depressive symptoms, and the pollution prevention actions alleviated the health deterioration of environmental pollution for the middle-aged and the elderly. It is imperative for the government to urgently reinforce policy enforcement to decrease air and water pollution and enhance the ability to circumvent pollution for the lower socioeconomic groups.

Particulate matter (PM2.5) and diseases: an autoregressive distributed lag (ARDL) technique

Air pollution can be attributed to the reduction in visibility, less agricultural activity, more health issues, and long-term destruction to infrastructure. This paper aimed to examine the validity of association among the Particulate matter (PM_2.5) and number of acute upper respiratory infection (ARI) and Asthma (AS) patients using an autoregressive distributed lag (ARDL) approach. This ARDL model study was conducted in Lahore, Pakistan. We used monthly data of ARI and AS patients acquired from Directorate General Health Services Punjab and PM_2.5 from Air Visual-IQAir during the period January 2018-August 2019. ARDL bound testing technique was used to investigate the association between number of AS, ARI patients and PM_2.5. In the short run, the PM_2.5 has substantial positive impact on number of AS patients in Lahore. The values of short-run coefficient depicts that the association between PM_2.5 and ARI patients is stronger than AS. The effect of PM_2.5 on number of patients in short term is more than that in the long-term. For both AS and ARI, in the long run, PM_2.5 has negative impact on number of patients.

Interactions of model airborne particulate matter with dipalmitoyl phosphatidylcholine and a clinical surfactant Calsurf

HYPOTHESIS

Lung surfactant protects lung tissue and reduces the surface tension in the alveoli during respiration. Particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5), which invades primely through inhalation, can deposit on and interact with the surfactant layer, leading to changes in the biophysical and morphological properties of the lung surfactant.

EXPERIMENTS

Langmuir monolayers of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) and clinical surfactant Calsurf were investigated with a PM_2.5 model injected into the water subphase, which were characterized by surface pressure-area isotherms, Brewster angle microscopy, atomic force microscopy, fluorescent microscopy, and x-ray photoelectron spectroscopy. The binding between DPPC/Calsurf and PM_2.5 was studied using isothermal titration calorimetry.

FINDINGS

PM_2.5 induced the expansion of the monolayers at low surface pressure (п) and film condensation at high п. Aggregation of PM_2.5 mainly occurred at the interface of liquid expanded/liquid condensed (LE/LC) phases. PM_2.5 led to slimmer and ramified LC domains on DPPC and the reduction of nano-sized condensed domains on Calsurf. Both DPPC and Calsurf showed fast binding with PM_2.5 through complex binding modes attributed to the heterogeneity and amphiphilic property of PM_2.5. This study improves the fundamental understanding of PM_2.5-lung surfactant interaction and shows useful implications of the toxicity of PM_2.5 through respiration process.

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

In this study, monthly particulate matter (PM_2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM_2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

Spatial distribution and health risk of exposure to BTEX in urban area: a comparison study of different land-use types and traffic volumes

Many previous studies have investigated BTEX concentrations in urban areas; however, the available evidence on the association of different land-use types and BTEX concentrations is still scarce. In this study, the BTEX concentrations were measured and compared in different land-use types and traffic volumes of Mashhad metropolis, Iran. Sampling was conducted in summer and winter of 2018 based on NIOSH 1501 method in six land-use types, including Residential, Commercial/official, Industrial, Greenspace, Transportation, and Tourism. The spatial autocorrelation model was used to investigate the emission pattern. The Monte Carlo simulation technique and sensitivity analysis were used to assess the health risk of exposure to BTEX compounds. The median [interquartile range (IQR)] of benzene, toluene, ethylbenzene m-xylene, o-xylene and total BTEX concentrations based on overall mean were 4 (2.23), 8.37 (4.48), 1.2 (1.46), 0.89 (2.59), 0.8 (1.73) and 17.7 (8.19) µg/m³, respectively. Benzene and toluene had clustered emission patterns (z-score > 1.96). Exposure to benzene in the study area had a carcinogenic risk for inhabitants. The concentration of BTEX compounds was significantly different based on land-use type. The maximum and minimum concentrations of BTEX were observed in Transportation and Greenspace land uses, respectively. The BTEX concentrations in summer were significantly higher than in winter, and traffic had a significant effect on BTEX concentrations. Overall, our results supported a significant relationship between land-use type and BTEX concentrations in the urban area. Moreover, ambient benzene concentration had a carcinogenic risk potential for inhabitants of study area.

Prenatal exposure to criteria air pollutants and associations with congenital anomalies: A Lebanese national study

Maternal exposure to air pollution has been associated with a higher birth defect (BD) risk. Previous studies suffer from inaccurate exposure assessment methods, confounding individual-level variations, and classical analytical modelling. This study aimed to examine the association between maternal exposure to criteria air pollutants and BD risk. A total of 553 cases and 10,214 controls were identified from private and public databases. Two subgroups were then formed: one for a matched case-control design, and another for Feature Selection (FS) analysis. Exposure assessment was based on the mean air pollutant-specific levels in the mother's residential area during the specific BD gestational time window of risk (GTWR) and other time intervals. Multivariate regression models outcomes consistently showed a significant protective effect for folic acid intake and highlighted parental consanguinity as a strong BD risk factor. After adjusting for these putative risk factors and other covariates, results show that maternal exposure to PM_2.5 during the first trimester is significantly associated with a higher overall BD risk (OR:1.05, 95%CI:1.01-1.09), and with a higher risk of genitourinary defects (GUD) (OR:1.06, 95%CI:1.01-1.11) and neural tube defects (NTD) (OR:1.10, 95%CI:1.03-1.17) during specific GTWRs. Maternal exposure to NO₂ during GTWR exhibited a significant protective effect for NTD (OR:0.94, 95%CI:0.90-0.99), while all other examined associations were not statistically significant. Additionally, maternal exposure to SO₂ during GTWR showed a significant association with a higher GUD risk (OR:1.17, 95%CI:1.08-1.26). When limiting selection to designated monitor coverage radiuses, PM_2.5 maintained significance with BD risk and showed a significant gene-environment interaction for GUD (p = 0.018), while NO₂ protective effect expanded to other subtypes. On the other hand, FS analysis confirmed maternal exposure to PM_2.5 and NO₂ as important features for GUD, CHD, and NTD. Our findings, set the basis for building a novel BD risk prediction model.

Is Compact Urban Form Good for Air Quality? A Case Study from China Based on Hourly Smartphone Data

In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms.

Impacts of urban land morphology on PM2.5 concentration in the urban agglomerations of China

Accurate understanding of the relationship between urban land morphology and the concentration of PM_2.5 is essential for achieving high-quality development of urban agglomerations. Based on a mechanism framework of "Internal-External driving force", 19 Chinese urban agglomerations at different development levels were analysed using the geographically weighted regression model to evaluate the impacts of urban land morphology on PM_2.5 concentrations in years 2000-2017. The results show: (1) The PM_2.5 average concentrations of all 19 urban agglomerations continue to increase from 30 μg/m³ in 2000 to 52 μg/m³ in 2007 but decreased to 34 μg/m³ in 2017. The changes in PM_2.5 concentrations vary for urban agglomerations at different development levels. Spatial differences in PM_2.5 concentrations are significant, forming a pattern that decreases from the centre to the periphery regions; (2) The urban land morphology of the entire urban agglomeration areas has undergone significant changes. The fractal dimension index (from 4.150 to 2.731) and the compactness (from 0.647 to 0.635) showed a downward trend, while the shape indices (from 1.421 to 1.606) demonstrated an increasing trend. National-level urban agglomerations are more compact and more complex in shape, while more fragmented are regional and local urban agglomerations; (3) Different parameters of urban land morphology have varying effects on PM_2.5 concentration varies and at different development levels of urban agglomerations. The combination of urban land morphology, socio-economic factors, and natural elements has a complex effect on PM_2.5 concentrations. It can contribute to understanding the linkage between urban land morphology and PM_2.5, providing references for future studies.

Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Tridirectional Transfer Learning for Predicting Gastric Cancer Morbidity

Our previous study has constructed a deep learning model for predicting gastrointestinal infection morbidity based on environmental pollutant indicators in some regions in central China. This article aims to adapt the prediction model for three purposes: 1) predicting the morbidity of a different disease in the same region; 2) predicting the morbidity of the same disease in a different region; and 3) predicting the morbidity of a different disease in a different region. We propose a tridirectional transfer learning approach, which achieves the abovementioned three purposes by: 1) developing a combined univariate regression and multivariate Gaussian model for establishing the relationship between the morbidity of the target disease and that of the source disease together with the high-level pollutant features in the current source region; 2) using mapping-based deep transfer learning to extend the current model to predict the morbidity of the source disease in both source and target regions; and 3) applying the pattern of the combined model in the source region to the extended model to derive a new combined model for predicting the morbidity of the target disease in the target region. We select gastric cancer as the target disease and use the proposed transfer learning approach to predict its morbidity in the source region and three target regions. The results show that, given only a limited number of labeled samples, our approach achieves an average prediction accuracy of over 80% in the source region and up to 78% in the target regions, which can contribute considerably to improving medical preparedness and response.

Calculation Methods of Emission Factors and Emissions of Fugitive Particulate Matter in South Korean Construction Sites

Recently, efforts to effectively reduce particulate matter by identifying its sources and trends have become necessary due to the sustained damage it has caused in East Asia. In the case of South Korea, damage due to fugitive dust generated at construction sites in densely populated downtown areas is significant, and particulate matter in such fugitive dust directly influences the health of nearby residents and construction workers. Accordingly, the purpose of the present study was to develop a method for calculating emission factors for PM10 and PM2.5 emission amounts in the fugitive dust generated in construction sites and to derive emission amount trends for major variables to predict the amounts of generated particulate matter. To this end, South Korean emission factors for PM10 and PM2.5 for different construction equipment and activities that generate fugitive dust were derived and a method for calculating the amount of particulate matter using the derived emission factors was proposed. In addition, the calculated total emissions using these factors were compared to those calculated using construction site fugitive dust equations developed for the United States, Europe, and South Korea, and the trend analysis of total emissions according to the major emission factor variables was conducted.

County-level variation in the long-term association between PM2.5 and lung cancer mortality in China

INTRODUCTION

The relative risk (RR) of long-term exposure to PM_2.5 in lung cancer mortality (LCM) may vary spatially in China. However, previous studies applying global regression have been unable to capture such variation. We aimed to employ a geographically weighted Poisson regression (GWPR) to estimate the RRs of LCM among the elderly (≥65 years) related to long-term exposure to PM_2.5 and the LCM attributable to PM_2.5 at the county level in China.

METHODS

We obtained annual LCM in the elderly between 2013 and 2015 from the National Death Surveillance. We linked annual mean concentrations of PM_2.5 between 2000 and 2004 with LCM using GWPR model at 148 counties across mainland China, adjusting for smoking and socioeconomic covariates. We used county-specific GWPR models to estimate annual average LCM in the elderly between 2013 and 2015 attributable to PM_2.5 exposure between 2000 and 2004.

RESULTS

The magnitude of the association between long-term exposure to PM_2.5 and LCM varied with county. The median of county-specific RRs of LCM among elderly men and women was 1.52 (range: 0.90, 2.40) and 1.49 (range: 0.88, 2.56) for each 10 μg/m³ increment in PM_2.5, respectively. The RRs were positively significant (P < 0.05) at 95% (140/148) of counties among both elderly men and women. Higher RRs of PM_2.5 among elderly men were located at Southwest and South China, and higher RRs among elderly women were located at Northwest, Southwest, and South China. There were 99,967 and 54,457 lung cancer deaths among elderly men and women that could be attributed to PM_2.5, with the attributable fractions of 31.4% and 33.8%, respectively.

CONCLUSIONS

The relative importance of long-term exposure to PM_2.5 in LCM differed by county. The results could help the government design tailored and efficient interventions. More stringent PM_2.5 control is urgently needed to reduce LCM in China.

Spatiotemporal Differences and Dynamic Evolution of PM2.5 Pollution in China

Air pollution, especially the urban haze, has become an urgent issue affecting the sustainable development of cities. Based on the PM2.5 concentration data of 225 Chinese cities collected by satellite remote sensing from 1998 to 2016, we quantitatively analyzed the spatiotemporal distribution characteristics and dynamic evolution trends of PM2.5 concentration in the four regions of China, namely the East, the Central, the West and the Northeast, by using statistical classification, GIS visualization, Dagum Gini coefficient decomposition and kernel density estimation. The results are as follows: First, the PM2.5 pollution in China showed a trend of fluctuation, which appeared to be increasing first and then decreasing, with the year 2007 as an important turning point for PM2.5 pollution changes across the country, as well as in the eastern and central regions. Second, PM2.5 pollution in China had significant spatial agglomeration. The intra-regional difference within the eastern region was the largest, and the inter-regional differences were the main source of overall differences. Third, kernel density estimation showed that the absolute difference of PM2.5 concentration distribution in China was expanding, with a significant phenomenon of polarization and the characteristics of spatial imbalance. This paper aimed to provide a scientific basis and effective reference for further advancing the sustainable development strategy of China in the new era.

Forecasting PM2.5-induced lung cancer mortality and morbidity at county level in China using satellite-derived PM2.5 data from 1998 to 2016: a modeling study

The serious ambient fine particulate matter (PM2.5) is one of the key risk factors for lung cancer. However, existing studies on the health effects of PM2.5 in China were less considered the regional transport of PM2.5 concentration. In this study, we aim to explore the association between lung cancer and PM2.5 and then forecast the PM2.5-induced lung cancer morbidity and mortality in China. Ridge regression (RR), partial least squares regression (PLSR), model tree-based (MT) regression, regression tree (RT) approach, and the combined forecasting model (CFM) were alternative forecasting models. The result of the Pearson correlation analysis showed that both local and regional scale PM2.5 concentration had a significant association with lung cancer mortality and morbidity and compared with the local lag and regional lag exposure to ambient PM2.5; the regional lag effect (0.172~0.235 for mortality; 0.146~0.249 for morbidity) was not stronger than the local lag PM2.5 exposure (0.249~0.294 for mortality; 0.215~0.301 for morbidity). The overall forecasting lung cancer morbidity and mortality were 47.63, 47.86, 39.38, and 39.76 per 100,000 population. The spatial distributions of lung cancer morbidity and mortality share a similar spatial pattern in 2015 and 2016, with high lung cancer morbidity and mortality areas mainly located in the central to east coast areas in China. The stakeholders would like to implement a cross-regional PM2.5 control strategy for the areas characterized as a high risk of lung cancer.

Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China

Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.

A load-carrier perspective examination on the change of ecological environment carrying capacity during urbanization process in China

Urbanization has prompted a dramatic social and economic development during the past decades in China. As a long-term national strategy, urbanization can only be implemented effectively with sufficient and sustainable ecological environment resources. By appreciating that the ecological environment carrying capacity (EECC) is a yardstick for guiding the practice of sustainable urban development, it is therefore pressing to examine the change of EECC adequately, so that the sustainable urbanization can be addressed appropriately. This paper develops a new method from load-carrier perspective to explore the change of EECC performance in the rapid urbanizing China. The EECC performance on water, land, atmosphere and overall perspectives were measured for 30 provinces in China based on the established method. The results show that most provinces in China are experiencing an improving EECC performance during the urbanization process, particularly with an obvious progress in land dimension. In referring to the spatial difference of overall EECC performance, the gap between 30 provinces has been narrowing during surveyed years. However, few provinces including Chongqing, Shandong and Jiangxi have undergone a degradation in overall EECC performance. The EECC performance in atmosphere dimension is still considered as a challenge faced by most provinces, evidenced by high level of PM2.5 concentration. These research findings provide valuable references not only for Chinese governments to formulate effective policy instruments and strategy measures for improving ecological environmental carrying status, but also for researchers to further study in the ecological environment carrying capacity in the context of other countries.

Spatio-Temporal Variations of Satellite-Based PM2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM_2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM_2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM_2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM_2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM_2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM_2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM_2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.

Ambient PM2.5 and Annual Lung Cancer Incidence: A Nationwide Study in 295 Chinese Counties

Most studies have examined PM2.5 effects on lung cancer mortalities, while few nationwide studies have been conducted in developing countries to estimate the effects of PM2.5 on lung cancer incidences. To fill this gap, this work aims to examine the effects of PM2.5 exposure on annual incidence rates of lung cancer for males and females in China. We performed a nationwide analysis in 295 counties (districts) from 2006 to 2014. Two regression models were employed to analyse data controlling for time, location and socioeconomic characteristics. We also examined whether the estimates of PM2.5 effects are sensitive to the adjustment of health and behaviour covariates, and the issue of the changing cancer registries each year. We further investigated the modification effects of region, temperature and precipitation. Generally, we found significantly positive associations between PM2.5 and incidence rates of lung cancer for males and females. If concurrent PM2.5 changes by 10 g/m³, then the incidence rate relative to its baseline significantly changes by 4.20% (95% CI: 2.73%, 5.88%) and 2.48% (95% CI: 1.24%, 4.14%) for males and females, respectively. The effects of exposure to PM2.5 were still significant when further controlling for health and behaviour factors or using 5 year consecutive data from 91 counties. We found the evidence of long-term lag effects of PM2.5. We also found that temperature appeared to positively modify the effects of PM2.5 on the incidence rates of lung cancer for males. In conclusion, there were significantly adverse effects of PM2.5 on the incidence rates of lung cancer for both males and females in China. The estimated effect sizes might be considerably lower than those reported in developed countries. There were long-term lag effects of PM2.5 on lung cancer incidence in China.

miRNAs deregulation in serum of mice is associated with lung cancer related pathway deregulation induced by PM2.5

Ambient fine particulate matter (PM2.5) as an environmental pollution has been associated with the lung cancer. However, the mechanism of epigenetics such as miRNAs deregulation between PM2.5-exposure and lung cancer has not been elucidated clearly. Twenty C57BL/6 mice were divided randomly into 2 groups and exposed to the filtered air (FA) and the concentrated air (CA), respectively. The FA mice were exposed to filtered air in chambers with a high-efficient particulate air filter (HEPA-filter), and the CA mice were exposed to concentration ambient PM2.5. The total duration of exposure was performed 6 h per day from December 1st, 2017 to January 27th, 2018. The mice exposed 900.21 μg/m³ PM2.5 for 6 h per day in CA chamber, which was nearly equaled to 225.05 μg/m³ for 24-h calculatingly. After exposure, the serum miRNAs levels were detected by microarray. Genetic and pathological alterations in lung of mice with/without PM2.5 exposure were detected. 38 differential miRNAs in serum of mice were found after PM2.5 exposure for 8 weeks. Among of them, 13 miRNAs related with lung cancer were consistent in serum and lung of mice. The target genes of 13 deregulated miRNAs including CRK, NR2F2, VIM, RASSF1, CCND2, PRKCA, SIRT1, CDK6, MAP3K7, HIF1A, UBE2V2, ATG10, BAX, E2F1, RASSF5 and CTNNB1, could involve in the pathway of lung cancer developing. Compared with the FA group, the significantly increases of histopathological changes, ROS and DNA damage were observed in lung of mice in CA group. Our study suggested that miRNAs in serum could be identified as candidate biomarkers to predict the lung cancer development during early PM2.5 exposure.

Air pollution and lung cancer incidence in China: Who are faced with a greater effect?

BACKGROUND

Whether socioeconomic indicators modify the relationship between air pollution exposure and health outcomes remains uncertain, especially in developing countries.

OBJECTIVE

This work aims to examine modification effects of socioeconomic indicators on the association between PM2.5 and annual incidence rate of lung cancer for males in China.

METHODS

We performed a nationwide analysis in 295 counties (districts) from 2006 to 2014. Using multivariable linear regression models controlling for weather conditions and socioeconomic indicators, we examined modification effects in the stratified and combined datasets according to the tertile and binary divisions of socioeconomic indicators. We also extensively investigated whether the roles of socioeconomic modifications were sensitive to the further adjustment of demographic factors, health and behaviour covariates, household solid fuel consumption, the different operationalization of socioeconomic indicators and PM2.5 exposure with single and moving average lags.

RESULTS

We found a stronger relationship between PM2.5 and incidence rate of male lung cancer in urban areas, in the lower economic or lower education counties (districts). If PM2.5 changes by 10 μg/m³, then the shift in incidence rate relative to its mean was significantly higher by 3.97% (95% CI: 2.18%, 4.96%, p = 0.000) in urban than in rural areas. With regard to economic status, if PM2.5 changes by 10 μg/m³, then the change in incidence rate relative to its mean was significantly lower by 0.99% (95% CI: -2.18%, 0.20%, p = 0.071) and 1.39% (95% CI: -2.78%, 0.00%, p = 0.037) in the middle and high economic groups than in the low economic group, respectively. The change in incidence rate relative to its mean was significantly lower by 1.98% (95% CI: -3.18%, -0.79%, p = 0.001) and 2.78% (95% CI: -4.17%, -1.39%, p = 0.000) in the middle and high education groups compared with the low education group, respectively, if PM2.5 changes by 10 μg/m³. We found no robust modification effects of employment rate and urbanisation growth rate.

CONCLUSION

Male residents in urban areas, in the lower economic or lower education counties are faced with a greater effect of PM2.5 on the incidence rate of lung cancer in China. The findings emphasize the need for public health intervention and urban planning initiatives targeting the urban-rural, educational or economic disparities in health associated with air pollution exposure. Future prediction on air pollution-induced health effects should consider such socioeconomic disparities, especially for the dominant urban-rural disparity in China.

Fugitive Road Dust PM2.5 Emissions and Their Potential Health Impacts

Fugitive road dust (FRD) particles emitted by traffic-generated turbulence are an important contributor to urban ambient fine particulate matter (PM_2.5). Especially in urban areas of developing countries, FRD PM_2.5 emissions are a serious environmental threat to air quality and public health. FRD PM_2.5 emissions have been neglected or substantially underestimated in previous study, resulting in the underestimation of modeling PM concentrations and estimating their health impacts. This study constructed the FRD PM_2.5 emissions inventory in a major inland city in China (Lanzhou) in 2017 at high-resolution (500 × 500 m²), investigated the spatiotemporal characteristics of the FRD emissions in different urban function zones, and quantified their health impacts. The FRD PM_2.5 emission was approximately 1141 ± 71 kg d^-1, accounting for 24.6% of total PM_2.5 emission in urban Lanzhou. Spatially, high emissions exceeding 3 × 10⁴ μg m^-2 d^-1 occurred over areas with smaller particle sizes, larger traffic intensities, and more frequent construction activities. The estimated premature mortality burden induced by FRD PM_2.5 exposure was 234.5 deaths in Lanzhou in 2017. Reducing FRD emissions are an important step forward to protect public health in many developing urban regions.

A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

Estimating the spatial distribution of environmental suitability for female lung cancer mortality in China based on a novel statistical method

Lung cancer as one of the major causes of cancer mortality has been demonstrated to be closely related to the ambient atmospheric environment, but little has been done in the synthetic evaluation of the linkage between cancer mortality and combined impact of ambient air pollution and meteorological conditions. The present study determined the environmental suitability for female lung cancer mortality associated with air contaminants and meteorological variables. A novel fuzzy matter-element method was applied to identify the spatial distribution and regions for the environmental suitability for the female lung cancer mortality across China in 2013. The membership functions between the cancer mortality and 6 environmental factors, including PM_2.5, NO₂, SO₂, PM₁₀, the annual mean wind speed, and mean temperature, were generated and the weights of each of the environmental factors were established by the maximum entropy (MaxEnt) model. We categorized the environmental suitability combined with GIS spatial analysis into three zones, including low-suitable, medium-suitable, and high-suitable region where the cancer mortality ranging from low to high rate was identified. These three zones were quantified by the MaxEnt model taking different air pollutants and meteorological variables into consideration. We identified that NO₂ was a most significant factor among the 6 environmental factors with the weight of 24.88%, followed by the annual mean wind speed, SO₂, and PM_2.5. The high-suitable area, mainly in the North China Plain which is a most heavily contaminated region by air pollution in China, covers 1.6195 million square kilometers, accounting for 17.85% of the total area investigated in this study. Identification of the impact of various environmental factors on cancer mortality in the different suitable area provides a scientific basis for the environmental management, risk assessment, and lung cancer control.

The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data

In recent years, haze pollution has become a serious environmental problem affecting cities in China. Reducing PM_2.5 concentrations through urban planning is a promising method that has been a focus of recent multidisciplinary research. Most existing studies only analyze the relationship between urban planning factors and PM_2.5 concentration, and it is difficult to accurately reflect residents' actual air pollution exposure without considering their space-time behaviors. This study uses satellite remote sensing and location service data to measure PM_2.5 pollution exposure in Wuhan metropolitan area and explores the effects of urban spatial structure, land use, spatial form, transportation, and green space on pollution exposure. The results show that spatial structure, building density, road density, and green space coverage have a significant impact on PM_2.5 pollution exposure. In addition, this study proposes corresponding implications for urban planning to improve public respiratory health.

Spatio⁻Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998⁻2016

A comprehensive understanding of the relationships between PM2.5 concentration and socioeconomic factors provides new insight into environmental management decision-making for sustainable development. In order to identify the contributions of socioeconomic development to PM2.5, their spatial interaction and temporal variation of long time series are analyzed in this paper. Unary linear regression method, Spearman's rank and bivariate Moran's I methods were used to investigate spatio⁻temporal variations and relationships of socioeconomic factors and PM2.5 concentration in 31 provinces of China during the period of 1998⁻2016. Spatial spillover effect of PM2.5 concentration and the impact of socioeconomic factors on PM2.5 concentration were analyzed by spatial lag model. Results demonstrated that PM2.5 concentration in most provinces of China increased rapidly along with the increase of socioeconomic factors, while PM2.5 presented a slow growth trend in Southwest China and a descending trend in Northwest China along with the increase of socioeconomic factors. Long time series analysis revealed the relationships between PM2.5 concentration and four socioeconomic factors. PM2.5 concentration was significantly positive spatial correlated with GDP per capita, industrial added value and private car ownership, while urban population density appeared a negative spatial correlation since 2006. GDP per capita and industrial added values were the most important factors to increase PM2.5, followed by private car ownership and urban population density. The findings of the study revealed spatial spillover effects of PM2.5 between different provinces, and can provide a theoretical basis for sustainable development and environmental protection.

Spatiotemporal patterns of recent PM2.5 concentrations over typical urban agglomerations in China

China experiences severe particulate matter pollution associated with rapid economic growth and accelerated urbanization. In this study, concentrations of PM_2.5 (fine particulate matter with an aerodynamic diameter ≤ 2.5 μm) throughout China, and specifically in nine typical urban agglomerations and one economic region, were statistically analyzed using high-resolution ground-based PM_2.5 observations from June 2014 to May 2018. The spatial variation of PM_2.5 was also explored via spatial autocorrelation analysis. High annual mean PM_2.5 concentrations were predominantly concentrated in the Beijing-Tianjin-Hebei, Central Plain, Northern Slope of Tianshan Mountain, and Cheng-Yu urban agglomerations, as well as the Huaihai Economic Region. The proportion of air quality nationwide monitoring sites where annual average PM_2.5 concentrations exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II annual standard were 82.8%, 77.1%, and 70.8% in 2015, 2016, and 2017, respectively. Moreover, the frequency of PM_2.5 concentrations meeting the CAAQS Grade I 24-h standard increased in five national-level urban agglomerations, and the average annual PM_2.5 decreased from 2015 to 2017 with a reduction rate of over 20%. The southern Beijing-Tianjin-Hebei agglomeration and surrounding areas revealed the highest PM_2.5 pollution in four seasons. Monthly mean PM_2.5 typically exhibited a characteristic "U" shape. Diurnal mean PM_2.5 concentrations were generally consistent with typical urban agglomerations, with maximum and minimum PM_2.5 values occurring at approximately 08:00-12:00 and 15:00-17:00, respectively, except for the Northern Slope of Tianshan Mountain urban agglomeration (NSTM-UA) (14:00 and 08:00, respectively). A positive spatial autocorrelation of PM_2.5 concentrations was observed in all urban agglomerations (except NSTM-UA); high-high agglomeration centers of PM_2.5 pollution were located far inland with a circular distribution, and low-low agglomeration centers formed at the periphery of the high-high agglomeration region. This study is key for understanding the difference in PM_2.5 concentrations among urban agglomerations and region-oriented air pollution control strategies are highly suggested.

Spatiotemporal patterns of aerosol optical depth throughout China from 2003 to 2016

With China's rapid economic growth, particle pollution, especially fine particulate matter (PM_2.5), which is known to have adverse health impacts, has become an increasingly serious issue. Satellite aerosol optical depth (AOD), an important physical property of aerosol particles, can serve as a proxy for investigating particle pollution because it can provide observations with comprehensive spatial and temporal coverage compared with ground-level measurements. This study used an improved 14-year high-resolution AOD dataset to examine the spatial characteristics and temporal dynamics of the dominant pollutants in China from 2003 to 2016 using advanced statistical methods. The improved AOD dataset combines the Moderate Resolution Imaging Spectroradiometer (MODIS) 3-km dark target AOD and 10-km deep blue AOD datasets, which enables a comparison of aerosol loading between eastern and western China. Pixel-based analysis indicates a significant difference between eastern and western China: high AOD values were generally observed in the east with a notable decrease, while low aerosol loadings were found in western China with no distinct change. The most particle polluted areas were the North China Plain, Hubei-Hunan region, Sichuan Basin, and Guangxi-Guangdong region in eastern China and western Qinghai and Tarim Basin in western China, with changes in the national AOD average center shifting to the northwest from 2013 to 2016. The impact factor analysis based on geographically weighted regression indicates that the effect of topography on the spatial characteristics of AOD is negative and more important in eastern China, which has low elevations. Built-up areas significantly exacerbate air pollution in the areas between eastern and western China, and there is no apparent AOD-vegetation relation dominates the country. This study thus provides a comprehensive understanding of the spatiotemporal variations of particle concentrations and can facilitate environmental management, policies to alleviate particle pollution, and health risk assessment studies.

Mortality and morbidity due to exposure to ambient particulate matter

The aim of this study was to investigate spatial variation and health risk of the exposure to PM_2.5 (particulate matter with a diameter of 2.5 µm or less) and PM₁₀ (particulate matter with a diameter of 10 µm or less) in Sabzevar, Iran. PM_2.5 and PM₁₀ were measured during three campaigns from April to November 2017, in 26 sampling points. Spatial analysis was performed using kriging and autocorrelations (Moran's index) model in Arc GIS software. Relationship between exposure to the PM_2.5 and PM₁₀ and their health impacts were investigated by AirQ 2.2.3 software. The mean concentrations (and standard deviation) of PM _2.5 and PM₁₀ over the entire study period were 32.54 (37.28) and 42.61 (47.76) μg/m³, respectively, which were higher than the guideline of World Health Organization. According to the spatial analysis, the maximum concentrations of PM_2.5 and PM₁₀ were around the main highway (beltway) which placed all over the south of Sabzevar. According to the Moran's index, the emission patterns for PM_2.5 (Z-score = 2.53; P-value = 0.011) and PM₁₀ (Z-score = 2.82; P-value = 0.004) were clustered. The attributable percentage (AP) of total mortality related to PM_2.5 and PM₁₀ were 3.544% (95% confidence interval (CI): 2.623-4.447%) and 2.055% (95% CI: 1.379-2.721%) per increasing each 10 μg/m³ of these pollutants, respectively. According to observed results, it is suggested that the beltway and other pollution sources, such as industries, should be placed at a greater distance from the city, to reduce PM amounts in residential areas.

Characteristics of public concern on haze in China and its relationship with air quality in urban areas

Severe air pollution associated with the rapid urbanization is a pressing issue in China. Moreover, the public awareness of environmental protection in China is awakening, which poses enormous pressure on governments to enforce environmental regulations. The study of environmental problems from the public perspective plays a crucial role in effective environmental governance. The Baidu search engine is the China's largest search engine. The search index of haze based on Baidu search engine reflects the public concern on air quality in China. The aim of this study is to uncover important relationships between public concern and air quality monitoring data based on the case study of haze pollution crisis in China. The results indicate that: (1) the year 2013 is the turning point of the public concern on air quality in China; (2) according to daily data analysis, the search index of haze has increased progressively with increased PM_2.5 concentration with a time lag of 0-4 days and the lag time has a declining tendency from 2013 to 2017; (3) according to annual data analysis, the public concern showed a weak correlation with air quality and they showed an opposite temporal trend. However, when the long-term annual trend was removed, the strong positive correlation emerges between the fluctuation parts of the search index of haze and monitoring data of air quality. This indicates the public is more sensitive to the short-term fluctuation of air quality. The results of this paper provide statistical evidence on the evolution of public concern on air quality from 2013 to 2017. This study will help policy makers to better understand the patterns of the public's perception of environmental problems and consequently improve the government's capability to deal with these challenges.

Lung cancer and particulate pollution: A critical review of spatial and temporal analysis evidence

BACKGROUND

Particulate matter (PM) has been recognized as one of the key risk factors of lung cancer. However, spatial and temporal patterns of this association remain unclear. Spatiotemporal analyses incorporate the spatial and temporal structure of the data within random effects models, generating more accurate evaluations of PM-lung cancer associations at a scale that can better inform lung cancer prevention programs.

METHODS

We conducted a critical review of spatial and temporal analyses of PM and lung cancer. The databases of PubMed, Web of Science and Scopus were searched for potential articles published until September 30, 2017. We included studies that applied spatial and temporal analyses to evaluate the associations of PM_2.5 (inhalable particles with diameters that are 2.5 µm and smaller) and PM₁₀ (inhalable particles with diameters that are 10 µm and smaller) with lung cancer.

RESULTS

We identified 17 articles eligible for the review. Of these, 11 focused on PM_2.5, five on PM₁₀, and one on both. These studies suggested a significant positive association between PM_2.5 exposure and the risk of lung cancer. Relative risks of lung cancer mortality ranged from 1.08 (95% confidence interval (CI): 1.07-1.09) to 1.60 (95%CI: 1.09-2.33) for 10 µg/m³ increase in PM_2.5 exposure. The association between PM₁₀ and lung cancer had been less well researched and the results were not consistent. In terms of the analysis methods, 16 papers undertook spatial analysis and one paper employed temporal analysis. No paper included spatial and temporal analyses simultaneously and considered spatiotemporal uncertainty into model predictions. Among the 16 papers with spatial analyses, thirteen studies presented maps, while only five and 11 studies utilized spatial exploration and modeling methods, respectively.

CONCLUSIONS

Advanced spatial and temporal epidemiological methods were seldom applied to PM-lung cancer associations. Further research is urgently needed to develop and employ robust and comprehensive spatiotemporal analysis methods for the evaluation of PM-lung cancer associations and the support of lung cancer prevention strategies.

Ventilation and Air Quality in Student Dormitories in China: A Case Study during Summer in Nanjing

The Air quality in student dormitories can have a major impact on the health of millions of students in China. This study aims to investigate the ventilation and air quality in student dormitories. Questionnaire survey was conducted in eight dormitory buildings and field measurements were conducted in one dormitory during the summer in Nanjing. The survey result reveals that most students thought the indoor and outdoor air quality was neutral and the correlation between indoor and outdoor perceived air quality is statistically significant. There are few indoor PM2.5 and ozone sources in dormitories and natural ventilation is the most common form of ventilation. However, there is no statistically significant correlation between window opening behaviors and the perceptions of indoor and outdoor air quality. The field measurement result shows the measured I/O ratios of PM2.5 and ozone over 37 days are in the range of 0.42⁻0.79 and 0.21⁻1.00, respectively. The I/O ratios for PM2.5 and ozone are 0.49 ± 0.05 and 0.26 ± 0.05 in the case of the window being closed, and the I/O ratios for PM2.5 and ozone are 0.65 ± 0.08 and 0.50 ± 0.15 in the case of the window being open. The outdoor and indoor ozone concentrations show pronounced diurnal periodic variations, while the PM2.5 concentrations do not. Finally, recommended open/close window strategies are discussed to reduce indoor pollutant levels. Understanding the indoor/outdoor PM2.5 and ozone concentrations in different window patterns can be a guidance to preventing high indoor PM2.5 and ozone exposure in student dormitories.