Physical environment, man-made pollution, nutrition and their mutual interactions can be major causes of human diseases. These disease determinants have distinct spatial distributions across geographical units, so that their adequate study involves the investigation of the associated geographical strata. We propose four geographical detectors based on spatial variation analysis of the geographical strata to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. In a real-world study, the primary physical environment (watershed, lithozone and soil) was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China). Basic nutrition (food) was found to be more important than man-made pollution (chemical fertilizer) in the control of the spatial NTD pattern. Ancient materials released from geological faults and subsequently spread along slopes dramatically increase the NTD risk. These findings constitute valuable input to disease intervention strategies in the region of interest.

a b s t r a c tThe outbreak of the 2019 novel coronavirus disease has caused more than 100,000 people to be infected and has caused thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations, and has caused widespread concern around the globe. In the fight against COVID-19, geographic information systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multisource big data, rapid visualization of epidemic information, spatial tracking of COVID-19, prediction of regional transmission, identification of the spatial allocation of risk and selection of the control level, balance and management of the supply and demand of medical resources, social-emotional guidance and panic elimination, the provision of solid spatial information support for decision-making about COVID-19 prevention and control, measures formulation, and assessment of the effectiveness of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against COVID-19, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy to provide accurate information for rapid social management. Additionally, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

Air pollutants from residential solid fuel combustion are attracting growing public concern. Field measured emission factors (EFs) of various air pollutants for solid fuels are close to the reality and urgently needed for better emission estimations. In this study, emission factors of particulate matter (PM), organic carbon (OC), elemental carbon (EC), and various polycyclic aromatic hydrocarbons (PAHs) from residential combustions of coal briquette, coal cake, and wood were measured in rural Heshun County, China. The measured EFs of PM, OC, and EC were 8.1–8.5, 2.2–3.6, 0.91–1.6 g/kg for the wood burnt in a simple metal stove, 0.54–0.64, 0.13–0.14, 0.040–0.0041 g/kg for the briquette burned in an improved stove with a chimney, and 3.2–8.5, 0.38–0.58, 0.022–0.052 g/kg for the homemade coal cake combusted in a brick stove with a flue, respectively. EFs of 28 parent PAHs, 4 oxygenated PAHs and 9 nitro-PAHs were 182–297, 7.8–10, 0.14–0.55 mg/kg for the wood, 14–16, 1.7–2.6, 0.64–0.83 mg/kg for the briquette, and 168–223, 4.7–9.5, 0.16–2.4 mg/kg for the coal cake, respectively. Emissions from the wood and coal cake combustions were much higher than those for the coal briquette, especially true for high molecular weight PAHs. Most EFs measured in the field were higher than those measured in stove combustions under laboratory conditions.

Background 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. Methods 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. Results 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 R 2 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 R 2 0.62, and all counties show statistically significant relationship. Conclusion 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.

BackgroundThe Hand-Foot-Mouth Disease (HFMD) is the most common infectious disease in China, its total incidence being around 500,000 ~1,000,000 cases per year. The composite space-time disease variation is the result of underlining attribute mechanisms that could provide clues about the physiologic and demographic determinants of disease transmission and also guide the appropriate allocation of medical resources to control the disease.Methods and FindingsHFMD cases were aggregated into 1456 counties and during a period of 11 months. Suspected climate attributes to HFMD were recorded monthly at 674 stations throughout the country and subsequently interpolated within 1456 × 11 cells across space-time (same as the number of HFMD cases) using the Bayesian Maximum Entropy (BME) method while taking into consideration the relevant uncertainty sources. The dimensionalities of the two datasets together with the integrated dataset combining the two previous ones are very high when the topologies of the space-time relationships between cells are taken into account. Using a self-organizing map (SOM) algorithm the dataset dimensionality was effectively reduced into 2 dimensions, while the spatiotemporal attribute structure was maintained. 16 types of spatiotemporal HFMD transmission were identified, and 3-4 high spatial incidence clusters of the HFMD types were found throughout China, which are basically within the scope of the monthly climate (precipitation) types.ConclusionsHFMD propagates in a composite space-time domain rather than showing a purely spatial and purely temporal variation. There is a clear relationship between HFMD occurrence and climate. HFMD cases are geographically clustered and closely linked to the monthly precipitation types of the region. The occurrence of the former depends on the later.

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Ecological niche model China's megacities Risk zones Early forecasting Recently, the coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Early and quick identification of the potential risk zones of COVID-19 infection is increasingly vital for the megacities implementing targeted infection prevention and control measures. In this study, the communities with confirmed cases during January 21-February 27 were collected and considered as the specific epidemic data for Beijing, Guangzhou, and Shenzhen. We evaluated the spatiotemporal variations of the epidemics before utilizing the ecological niche models (ENM) to assemble the epidemic data and nine socioeconomic variables for identifying the potential risk zones of this infection in these megacities. Three megacities were differentiated by the spatial patterns and quantities of infected communities, average cases per community, the percentages of imported cases, as well as the potential risks, although their COVID-19 infection situations have been preliminarily contained to date. With higher risks that were predominated by various influencing factors in each megacity, the potential risk zones coverd about 75% to 100% of currently infected communities. Our results demonstrate that the ENM method was capable of being employed as an early forecasting tool for identifying the potential COVID-19 infection risk zones on a fine scale. We suggest that local hygienic authorities should keep their eyes on the epidemic in each megacity for sufficiently implementing and adjusting their interventions in the zones

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping.