Hand, foot and mouth disease (HFMD) is a common infectious disease, causing thousands of deaths among children in China over the past two decades. Environmental risk factors such as meteorological factors, population factors and economic factors may affect the incidence of HFMD. In the current paper, we used a novel model—geographical detector technique to analyze the effect of these factors on the incidence of HFMD in China. We collected HFMD cases from 2,309 counties during May 2008 in China. The monthly cumulative incidence of HFMD was calculated for children aged 0–9 years. Potential risk factors included meteorological factors, economic factors, and population density factors. Four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) were used to analyze the effects of some potential risk factors on the incidence of HFMD in China. We found that tertiary industry and children exert more influence than first industry and middle school students on the incidence of HFMD. The interactive effect of any two risk factors increases the hazard for HFMD transmission.

Over the past two decades, major epidemics of hand, foot, and mouth disease (HFMD) have occurred throughout most of the West-Pacific Region countries, causing thousands of deaths among children. However, few studies have examined potential determinants of the incidence of HFMD. Reported HFMD cases from 2912 counties in China were obtained for May 2008. The monthly HFMD cumulative incidence was calculated for children aged 9 years and younger. Child population density (CPD) and six climate factors (average-temperature [AT], average-minimum-temperature [AT_min], average-maximum-temperature [AT_max], average-temperature-difference [AT_diff], average-relative-humidity [ARH], and monthly precipitation [MP]) were selected as potential explanatory variables for the study. Geographically weighted regression (GWR) models were used to explore the associations between the selected factors and HFMD incidence at county level. There were 176,111 HFMD cases reported in the studied counties. The adjusted monthly cumulative incidence by county ranged from 0.26 cases per 100,000 children to 2549.00 per 100,000 children. For local univariate GWR models, the percentage of counties with statistical significance (p<0.05) between HFMD incidence and each of the seven factors were: CPD 84.3%, AT_max 54.9%, AT 57.8%, AT_min 61.2%, ARH 54.4%, MP 50.3%, and AT_diff 51.6%. The R² for the seven factors’ univariate GWR models are CPD 0.56, AT_max 0.53, AT 0.52, MP 0.51, AT_min 0.52, ARH 0.51, and AT_diff 0.51, respectively. CPD, MP, AT, ARH and AT_diff were further included in the multivariate GWR model, with R² 0.62, and all counties show statistically significant relationship. Child population density and climate factors are potential determinants of the HFMD incidence in most areas in China. The strength and direction of association between these factors and the incidence of HFDM is spatially heterogeneous at the local geographic level, and child population density has a greater influence on the incidence of HFMD than the climate factors.

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM_2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM_2.5 observation station of the U.S Embassy and the 18 PM₁₀ observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM_2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM_2.5 monitoring station and at the 18 official PM₁₀ stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM_2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.

Global large-scale urbanization has a deep impact on climate change and has brought great challenges to sustainable development, especially in urban agglomerations. At present, there is still a lack of research on the quantitative assessment of the relationship between urban scale and urban expansion and the degree of the urban heat island (UHI) effect, as well as a discussion on mitigation and adaptation of the UHI effect from the perspective of planning. This paper analyzes the regional urbanization process, average surface temperature variation characteristics, surface urban heat island (SUHI), which reflects the intensity of UHI, and the relationship between urban expansion, urban scale, and the UHI in the Beijing–Tianjin–Hebei (BTH) urban agglomeration using multi-source analysis of data from 2000, 2005, 2010, and 2015. The results show that the UHI effect in the study area was significant. The average surface temperature of central areas was the highest, and decreased from central areas to suburbs in the order of central areas > expanding areas > rural residential areas. From the perspective of spatial distribution, in Beijing, the southern part of the study area, the junction of Tianjin, Langfang, and Cangzhou are areas with intense SUHI. The scale and pace of expansion of urban land in Beijing were more than in other cities, the influencing range of SUHI in Beijing increased obviously, and the SUHI of central areas was most intense. The results indicate that due to the larger urban scale of the BTH urban agglomeration, it will face a greater UHI effect. The UHI effect was also more significant in areas of dense distribution in cities within the urban agglomeration. Based on results and existing research, planning suggestions are proposed for central areas with regard to expanding urban areas and suburbs to alleviate the urban heat island effect and improve the resilience of cities to climate change.