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Background: Air pollution is a significant risk factor for breast cancer. However, epidemiological studies have yielded inconsistent results. Previous systematic reviews and meta-analyses have identified an association between nitrogen dioxide (NO2) exposure and the incidence of breast cancer, yet no consistent association has been observed for particulate matter (PM). With recent studies providing new evidence, updated meta-analyses are necessary. Methods: Relevant studies were identified through comprehensive literature searches in the Web of Science and PubMed databases. The pooled effect estimates for the associations of NO2, PM2.5, and PM10 with breast cancer incidence were calculated using a random-effects model. Publication bias was corrected, and sensitivity analyses were conducted to ensure the robustness of the findings. Subgroup analyses were performed based on menopausal status, hormone receptor subtype, and study region. Results: A total of 22 studies were included in this meta-analysis. The pooled hazard ratios with 95 % confidence intervals (CIs) for breast cancer incidence per 10 μg/m3 increase in NO2 and PM2.5 were 1.02 (95 % CI: 1.01, 1.03) and 1.06 (95 % CI: 1.02, 1.11), respectively. No significant association was observed between PM10 and breast cancer incidence. NO2 and PM10 exposures were significantly associated with the incidence of postmenopausal breast cancer. The associations of NO2, PM2.5, and PM10 with estrogen/progesterone receptor-positive (ER+/PR+) breast cancer were not significantly different from those with estrogen/progesterone receptor-negative (ER–/PR–) breast cancer. Exposure to both NO2 and PM2.5 was associated with breast cancer incidence in Europe, whereas in North America, only NO2 exposure showed a significant association. Conclusion: This study is the first to document a significant association between long-term exposure to ambient PM2.5 and breast cancer incidence through meta-analysis. Air pollution has a pronounced impact on postmenopausal breast cancer, and the strength of the association between specific air pollutants and breast cancer incidence varies across regions. These findings suggest that long-term exposure to NO2 and PM2.5 may increase the incidence of breast cancer.

Spatial heterogeneity is a fundamental research topic in the field of geography, and Geodetector is a widely used tool for studying this phenomenon. To understand the research advancements and knowledge diffusion trends surrounding Geodetector, we constructed an author evolutionary tree structure fusing its 847 core citations in the Web of Science database and Geotree model for the first time. The results of our literature statistics indicated that Geodetector has garnered the attention of 3123 authors from 48 countries since its publication in the Ecological Indicators journal in 2010, who have published core papers concerning ten important topics. The majority of these studies focused on spatial heterogeneity and its influencing factors. Our analysis of Geotree data revealed a significant correlation between the publication rate of scholars in large teams and their academic activities. Our analysis of the knowledge diffusion chain shown that only 2% of the total number of authors have contributed to over 20% of the scientific collaborations and knowledge diffusion, and they were recognized as experts in Geodetector research. To provide a comprehensive reference for future scholars, we have summarized the citing countries, five classical articles, main scientific domains, and core teams of Geodetector research.

The environmental Kuznets curve (EKC) is often used to analyze the relationship between environmental pollution health indicators and economic development level in different regions. In developed countries, the blood lead levels (BLLs) of children have been declining fitting the EKC since the 1970s. However, such figures in China have remained at relatively high levels, without any obvious decline, since 2010. We explored spatial variations and graded countermeasures using reported data on BLLs including the lead poisoning rates (LPRs) of children. We found that there were prefectures where either the mean BLLs of the children had reached 100.00 μg/L or the LPRs of more than 40% of the children had reached 100 μg/L. When we reduced the average BLLs to 50 μg/L or lowered the proportion of children with a lead poisoning rate (LPR) above 100 μg/L to 10.00%, the EKC trend decreased, and the linear slope after 2010 became -0.202. If the areas where children's BLLs exceeded 50.00 μg/L or the proportion of children with an LPR above 100 μg/L was more than 10.00% will be controlled, the linear slope of the EKC decrease trend after 2010 will became -0.989, and the national average of children's BLLs would decline by 22.17%. The study concluded that children's BLLs in different regions of China are situated at different EKC stages, and urgent prevention and control strategies must be put in place for undeveloped areas.

Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods.

Summary: Background: Social determinants of health (SDOH) produce a broad range of life expectancy (LE) disparities. In China, limited literatures were found to report association between SDOH and LE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between SDOH and LE in China. Methods: Provincial-level LE were estimated from mortality records during 2005–2020 from National Mortality Surveillance System in China. A spatial panel Durbin model was used to investigate LE associated SDOH proxies. Spatial spillover effects were introduced to interpret direct and indirect effects caused by SDOH during long-term and short-term period on LE disparities. Findings: Nationwide, LE increased from 73.1 (95% confidence interval (CI): 71.3, 74.4) years to 77.7 (95%CI: 76.5, 78.7) years from 2005 to 2020. Unequally spatial distribution of LE with High-High clustering in coastal areas and Low-Low clustering in western regions were observed. Locally, it was estimated that SDOH proxies statistically significant related to an increase of LE, including GDP (coefficient: 0.02, 95%CI: 0.00, 0.03), Gini index (coefficient: 2.35, 95%CI: 1.82, 2.88), number of beds in health care institutions (coefficient: 0.02, 95%CI: 0.00, 0.05) and natural growth rate of resident population (coefficient: 0.02, 95%CI: 0.01, 0.02). Direct and indirect effects decomposition during long-term and short-term of LE associated SDOH proxies demonstrated that GDP, urbanization rate, unemployment rate, education attainment, Gini index, number of beds in health care institutions, sex ratio, gross dependence ratio and natural growth rate of resident population not only affected local LE, but also exerted spatial spillover effects towards geographical neighbors. Interpretation: Spatial variations of LE existed at provincial-level in China. SDOH regarding socioeconomic development and equity, healthcare resources, as well as population characteristics not only affected LE disparities at local scale but also among nearby provinces. Externalities of policy of those SDOH proxies should be took into consideration to promote health equity nationally. Comprehensive approaches on the basis of population strategy should be consolidated to optimize supportive socioeconomic environment and narrow the regional gap to reduce health disparities and increase LE. Funding: National Key Research & Development Program of China (Grant No.2018YFC1315301); Ministry of Education of China Humanities and Social Science General Program (Grant No.18YJC790138).

Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K-means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.