To quantify the associations between the spatial characteristics of hand, foot, and mouth disease (HFMD) epidemic and meteorological factors (average temperature (AT), relative humidity (RH), average pressure (AP), average wind speed (AW) and average rainfall (AR)), child population density (CPD) and Per capita GDP (GDP) in Inner Mongolia Autonomous Region, China, and to detect the variation of influence in different seasons and counties, geographically weighted regression (GWR) model was constructed. The monthly cumulative incidence (CI) of HFMD was worked out for children ≤9 years from June to December, 2016. The results revealed that GWR model had a far superior goodness-of-fit for describing the relationship between the risk factors and HFMD incidence. Meteorological factors had different significance in their effect on HFMD incidence depending on the season. AT and AR had the greatest impact on HFMD in summer. The influence of RH on HFMD was significant in early autumn. AW was negatively correlated with HFMD in summer and positively correlated in autumn and winter. The effects of AW and AP on the incidence of HFMD were statistically significant in winter. GDP and CPD were not significantly related to HFMD occurrence for most time periods.

Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children’s health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons.

It has been reported that cardiovascular disease (CVD) has become one of the major threats to global public health and is associated with climate change. Several previous studies have shown the influence of ambient temperature on CVD, but lack some evidence for the short-term effect of diurnal temperature range (DTR) on CVD mortality in northeast China. This is the first study to assess the correlation between DTR and CVD mortality in Hulunbuir located in northeast China. Daily CVD mortality data and meteorological data were collected from 2014 to 2020. A quasi-Poisson generalized linear regression with a distributed lag non-linear model (DLNM) was applied to exploring the short-term impact of DTR on CVD mortality. Stratified analyses by gender, age, and season were conducted and the short-term impacts of extremely high DTR on CVD mortality were investigated. In this study, a total of 21,067 CVD mortality cases were recorded in Hulunbuir, China from 2014 to 2020. Compared to the reference value (11.20 $$^{\circ }$$∘ C, 50 $${\text{ th }}$$th percentile), a “U-shaped” non-linear relationship between DTR and CVD mortality was observed, and extremely high DTR increased the risk of CVD mortality. The short-term effect of extremely high DTR occurred immediately and lasted up to 6 days. In addition, the male and the age $$\ge$$≥ 65 groups were more likely to be affected by extremely high DTR compared with the female and the age < 65 groups, respectively. The results also showed that extremely high DTR in cold season had a more adverse effect on CVD mortality than warm season. This study suggests that extremely high DTR for cold season should be paid enough attention to for residents in northeast China. The male and the age $$\ge$$≥ 65 groups were more vulnerable to the impacts of DTR. The study results may provide some suggestions for decision-making by local public health authorities to avoid the adverse impacts of high DTR, and improve the health of residents, especially vulnerable groups in cold season.

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature dataset was established using remote sensing imagery and meteorological observations. Spatial features were extracted through a convolutional neural network (CNN), while the temporal evolution of sandstorms was modeled using a bidirectional long short-term memory (BiLSTM) network. A random forest algorithm was employed to assess the relative importance of meteorological and geographical factors. The results indicate that the proposed CNN-BiLSTM model achieved strong performance at prediction intervals of 1, 6, 12, 18, and 24 h, with overall accuracy, F1-score, and AUC all exceeding 0.80. The 24 h prediction yielded the best results, with evaluation metrics of 0.861, 0.878, and 0.898, respectively. Compared with the individual CNN and BiLSTM models, the CNN-BiLSTM model demonstrated superior performance. The findings suggest that the model provides high predictive accuracy and stability across different time steps, thereby offering strong support for dust storm path prediction on the Mongolian Plateau and contributing to the reduction of disaster-related risks and losses.