Combining cosmic-ray neutron sensor and fallout 137Cs to explore the connection of soil water content with soil redistribution in an agroforestry hillslope.

To ensure sustainable agricultural management, there is a need not only to quantify soil erosion rates but also to obtain information on the status of soil water content and soil loss under different soil types and land uses. A clear understanding of the temporal dynamics and the soil moisture spatial variability (SMSV) will help to control soil degradation by hydrological processes. This study represents the first attempt connecting cosmic-ray neutron sensors (CRNS) with soil erosion research, a novel approach to explore the complex relationships between soil water content (SWC) and soil redistribution processes using two of the most powerful nuclear techniques, CRNS and fallout 137Cs. Our preliminary results indicate that CRNS captured soil moisture dynamics along the study toposequence and demonstrated the sensitivity of neutron sensors to investigate the effect of parent material on soil water content. The Empirical Orthogonal Function (EOF) analysis of the comprehensive data from seven CRNS surveys revealed that one dominant spatial structure (EOF1) explains 89.2% of SMSV. The soil redistribution rates estimated with 137Cs at the nine locations along the hillslope, together with local factors related to soil properties (SOC, soil depth, hydraulic conductivity) and land use showed significant correlations with EOF. This study provides strong field evidence that soil type significantly affect SMSV, highlighting the key impact on soil erosion and sedimentation rates. Nevertheless, more research is needed to investigate the specific contributions of soil properties to the spatial variability of soil moisture and their subsequent effects on soil redistribution dynamics of interest for soil management.

Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections.

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank-reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub-fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population-scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum-likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early-life survival ("recruitment") of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end-of-century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta-correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades.

Temporal-Spatial Surface Seasonal Mass Changes and Vertical Crustal Deformation in South China Block from GPS and GRACE Measurements.

The solid Earth deforms elastically in response to variations of surface atmosphere, hydrology, and ice/glacier mass loads. Continuous geodetic observations by Global Positioning System (CGPS) stations and Gravity Recovery and Climate Experiment (GRACE) record such deformations to estimate seasonal and secular mass changes. In this paper, we present the seasonal variation of the surface mass changes and the crustal vertical deformation in the South China Block (SCB) identified by GPS and GRACE observations with records spanning from 1999 to 2016. We used 33 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs) in SCB. The average weighted root-mean-square (WRMS) reduction is 38% when we subtract GRACE-modeled vertical displacements from GPS time series. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution in and around the South China Block. The correlation between GRACE and GPS time series is analyzed which provides a reference for further improvement of the seasonal variation of CGPS time series. The results of the GRACE observations inversion are the surface deformations caused by the surface mass change load at a rate of about -0.4 to -0.8 mm/year, which is used to improve the long-term trend of non-tectonic loads of the GPS vertical velocity field to further explain the crustal tectonic movement in the SCB and surroundings.

Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.

In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.

Seasonal Mass Changes and Crustal Vertical Deformations Constrained by GPS and GRACE in Northeastern Tibet.

Surface vertical deformation includes the Earth's elastic response to mass loading on or near the surface. Continuous Global Positioning System (CGPS) stations record such deformations to estimate seasonal and secular mass changes. We used 41 CGPS stations to construct a time series of coordinate changes, which are decomposed by empirical orthogonal functions (EOFs), in northeastern Tibet. The first common mode shows clear seasonal changes, indicating seasonal surface mass re-distribution around northeastern Tibet. The GPS-derived result is then assessed in terms of the mass changes observed in northeastern Tibet. The GPS-derived common mode vertical change and the stacked Gravity Recovery and Climate Experiment (GRACE) mass change are consistent, suggesting that the seasonal surface mass variation is caused by changes in the hydrological, atmospheric and non-tidal ocean loads. The annual peak-to-peak surface mass changes derived from GPS and GRACE results show seasonal oscillations in mass loads, and the corresponding amplitudes are between 3 and 35 mm/year. There is an apparent gradually increasing gravity between 0.1 and 0.9 µGal/year in northeast Tibet. Crustal vertical deformation is determined after eliminating the surface load effects from GRACE, without considering Glacial Isostatic Adjustment (GIA) contribution. It reveals crustal uplift around northeastern Tibet from the corrected GPS vertical velocity. The unusual uplift of the Longmen Shan fault indicates tectonically sophisticated processes in northeastern Tibet.

Spatiotemporal pattern of hand-foot-mouth disease in China: an analysis of empirical orthogonal functions.

OBJECTIVES: Hand-foot-mouth disease (HFMD) is the most common infectious disease in China. Spatial and temporal patterns of HFMD in China provide valuable information on the relationship between HFMD and the geographical environment, and help in the prediction of HFMD transmission. STUDY DESIGN: Cross-sectional study. METHODS: Total HFMD morbidity per 10 days from May 2008 to March 2009 was recorded in 1966 counties in China. Empirical orthogonal function (EOF) analysis was used to obtain spatial and temporal patterns of HFMD. RESULTS: The first five modes of HFMD morbidity explained 84.24% of the total variance. The dominant mode (first mode showing the highest variance) showed high HFMD morbidity in the western counties of Bohai Bay, the mid-south of China, the Yangtze River delta, the Pearl River delta and the areas bordering Vietnam from early May to late July 2008. The second mode showed high HFMD morbidity in the western counties of Bohai Bay, the north-east of China, north of Xinjiang and the Yangtze River delta from late May to the middle of August 2008. The third mode showed high HFMD morbidity in the Yangtze River delta, the Pearl River delta and the middle of the Huaihe River basin in early May 2008. CONCLUSIONS: EOF analysis of HFMD morbidity shows the main spatiotemporal patterns and can explain variance in HFMD in China.

Spatiotemporal changes and influencing factors of water clarity in the Yellow Sea over the past 20 years.

Water transparency is an important parameter for describing the optical properties of water. It reflects changes in marine environment and is of great significance for guiding the development and protection of marine environment. In this study, based on the algorithm proposed by Lee et al. in 2015, water transparency in the Yellow Sea from 2003 to 2022 was inverted. The results revealed that, in terms of spatial distribution, the water in western region of the Yellow Sea had relatively low transparency, whereas the water in the central and southern regions had high transparency. Regarding temporal trends, declining transparency was observed throughout most of the study period, but the trends reversed and transparency began to increase in 2017. Empirical orthogonal function analysis confirmed that water transparency was primarily influenced by the optical constituents of water. Long-term monitoring of water clarity is of significant importance for the preservation of marine ecological environments.

Statistical-dynamical modeling of the maize yield response to future climate change in West, East and Central Africa using the regional climate model REMO.

Africa is vulnerable to the impacts of climate change, particularly in terms of its agriculture and crop production. The majority of climate models project a negative impact of future climate change on crop production, with maize being particularly vulnerable. However, the magnitude of this change remains uncertain. Therefore, it is important to reduce the uncertainties related to the anticipated changes to guide adaptation options. This study uses a combination of local and large-scale empirical orthogonal function (EOF) predictors as a novel approach to model the impacts of future climate change on crop yields in West, East and Central Africa. Here a cross-validated Bayesian model was developed using predictors derived from the regional climate model REMO for the period 1982-2100. On average, the combined local and large-scale EOF predictors explained around 28 % of maize yield variability from 1982 to 2016 of the entire study regions. Notably, climate predictors played a significant role in West Africa, explaining up to 51 % of the maize yield variability. Large-scale climate EOF predictors contributed most to the explained variance, reflecting the role of regional climate in future maize yield variability. Under a high-emissions scenario (RCP8.5), maize yield is projected to decrease over the entire study region by 20 % by the end of the century. However, a minor increase is projected in eastern Africa. This study highlights the importance of incorporating climate predictors at various scales into crop yield modeling. Furthermore, the findings will offer valuable guidance to decision-makers in shaping adaptation options.

[Spatial-temporal Variation and Driving Factors of Ozone in China from 2019 to 2021 Based on EOF Technique and KZ Filter].

Based on the hourly O3 concentration data of 337 prefectural-level divisions and simultaneous surface meteorological data in China, we applied empirical orthogonal function (EOF) analysis to analyze the main spatial patterns, variation trends, and main meteorological driving factors of O3 concentration in China from March to August in 2019-2021. In this study, a KZ (Kolmogorov-Zurbenko) filter was used to decompose the time series of O3 concentration and simultaneous meteorological factors into corresponding short-term, seasonal, and long-term components in 31 provincial capitals.Then, the stepwise regression was used to establish the relationship between O3 and meteorological factors. Ultimately, the long-term component of O3 concentration after "meteorological adjustment" was reconstructed. The results indicated that the first spatial patterns of O3 concentration showed a convergent change, that is, the volatility of O3 concentration was weakened in the high-value region of variability and enhanced in the low-value region.Before and after the meteorological adjustment, the variation trend of O3 concentration in different cities was different to some extent. The adjusted curve was "flatter" in most cities. Among them, Fuzhou, Haikou, Changsha, Taiyuan, Harbin, and Urumqi were greatly affected by emissions. Shijiazhuang, Jinan, and Guangzhou were greatly affected by meteorological conditions. Beijing, Tianjin, Changchun, and Kunming were greatly affected by emissions and meteorological conditions.

[Temporal and Spatial Variations in Ozone and Its Causes over Hainan Province from 2015 to 2020].

This study investigated temporal and spatial variations in O3-8h (defined as the maximum 8 h average result) in Hainan Province from 2015 to 2020 and further analyzed its relationships with precursors and meteorological factors based on a dataset of observations from 32 environmental monitoring stations in Hainan. Basic statistical methods, including the empirical orthogonal function (EOF), climatic tendency rate, and climatic trend coefficient analysis, were used here. The results showed that ρ(O3-8h) was higher in northern and western Hainan than that in other regions, with the maximum value occurring in Dongfang City (91.5 µg·m-3). Twelve cities and counties experienced a downward trend from 2015 to 2020, and six cities and counties reached a 95% confidence level. The variation in ρ(O3-8h) in Hainan Province demonstrated remarkable seasonal changes, which were the largest in the autumn, spring, and winter followed by the smallest in the summer, exhibiting a clear declining trend in all seasons except autumn. In addition, the cumulative variance of the first two eigenvector fields decomposed by EOF was 72.58%, which could well describe the distributed characteristics of ρ(O3-8h) in Hainan Province. The first mode reflected the consistency of ρ(O3-8h) variation, and the second mode reflected regional differences. Meanwhile, the change in ρ(O3-8h) had a good correlation with the precursors and meteorological factors. Among them, the correlation coefficients between ρ(O3-8h) and ρ(NO2), precipitation, sunshine duration, average temperature, average wind speed, atmospheric pressure, and total radiation passed the 99% confidence test. The results of multiple linear regression showed that the variation in regressed ρ(O3-8h) was consistent with the observed ρ(O3-8h), and the correlation coefficient between them was 0.853, which passed the 99.9% confidence test. The regression value explained 0.72 variance of the observed value.

Space-time dynamics of soil moisture and groundwater in an agriculture-dominated critical zone observatory (CZO) in the Ganga basin, India.

Space-time variability of soil moisture (SM) and ground water plays a fundamental role in shaping hydrology of terrestrial ecosystem, best represented as the Critical Zone (CZ), which extends from top of vegetation canopy to the bottom of groundwater table. In several parts of the world, a network of instrumented sites, known as Critical Zone Observatories (CZOs), have been set up to understand the hydrodynamics of soil-water system in particular reference to natural and anthropogenic forcings. Here, we employed the empirical orthogonal function (EOF), random combination, and temporal stability approach to understand the in-situ space-time dynamics of SM and depth to groundwater table (DTGT) over an agriculture-dominated CZO in the Ganga basin. Our results showed that both the components exhibit a constant temporal coefficient of variation, suggesting a consistent seasonal changing pattern. Around 91 % of the observed DTGT spatial variation are explained by first two spatial EOFs while the first five EOFs explain only 67 % of the total SM variability. On an annual basis, the spatial patterns of SM and DTGT are driven by topography and soil texture (% clay) while monsoon rainfall and post-monsoon crop cycle appear to be the leading factors for temporal variability of both components. Furthermore, we have demonstrated that randomly selected four sampling locations and three monitoring wells within the CZO could capture the mean spatial variability of SM (RMSE = 3 % vol/vol) and DTGT (RMSE = 0.7 mgbl) respectively. In addition, temporal stability analysis indicates that four representative sites and a single monitoring well can provide robust catchment mean with an absolute error of ±2 % vol/vol and 0.36 mgbl respectively. Overall, this study provides an insight to the hydrodynamics and controls of SM and groundwater in an agricultural landscape with significant implications for upscaling and efficient water resource management in such regions.

Deep learning prediction of two-dimensional ocean dynamics with wavelet-compressed data.

This study addresses the challenge represented by the application of deep learning models to the prediction of ocean dynamics using datasets over a large region or with high spatial or temporal resolution In a previous study by the authors of this article, they showed that such a challenge could be met by using a divide and conquer approach. The domain was in fact split into multiple sub-regions, which were small enough to be predicted individually and in parallel with each other by a deep learning model. At each time step of the prediction process, the sub-model solutions would be merged at the boundary of each sub-region to remove discontinuities between consecutive domains in order to predict the evolution of the full domain. This approach led to the growth of non-dynamical errors that decreased the prediction skill of our model. In the study herein, we show that wavelets can be used to compress the data and reduce its dimension. Each compression level reduces by a factor of two the horizontal resolution of the dataset. We show that despite the loss of information, a level 3 compression produces an improved prediction of the ocean two-dimensional data in comparison to the divide and conquer approach. Our method is evaluated on the prediction of the sea surface height of the most energetic feature of the Gulf of Mexico, namely the Loop Current.

Assessment of Tidal Range Changes in the North Sea From 1958 to 2014.

We document an exceptional large-spatial scale case of changes in tidal range in the North Sea, featuring pronounced trends between -2.3 mm/yr at tide gauges in the United Kingdom and up to 7 mm/yr in the German Bight between 1958 and 2014. These changes are spatially heterogeneous and driven by a superposition of local and large-scale processes within the basin. We use principal component analysis to separate large-scale signals appearing coherently over multiple stations from rather localized changes. We identify two leading principal components (PCs) that explain about 69% of tidal range changes in the entire North Sea including the divergent trend pattern along United Kingdom and German coastlines that reflects movement of the region's semidiurnal amphidromic areas. By applying numerical and statistical analyses, we can assign a baroclinic (PC1) and a barotropic large-scale signal (PC2), explaining a large part of the overall variance. A comparison between PC2 and tide gauge records along the European Atlantic coast, Iceland, and Canada shows significant correlations on time scales of less than 2 years, which points to an external and basin-wide forcing mechanism. By contrast, PC1 dominates in the southern North Sea and originates, at least in part, from stratification changes in nearby shallow waters. In particular, from an analysis of observed density profiles, we suggest that an increased strength and duration of the summer pycnocline has stabilized the water column against turbulent dissipation and allowed for higher tidal elevations at the coast.

A comparative study of EOF and NMF analysis on downward trend of AOD over China from 2011 to 2019.

In recent decades China has experienced high-level PM2.5 pollution and then visible air quality improvement. To understand the air quality change from the perspective of aerosol optical depth (AOD), we adopted two statistical methods of Empirical Orthogonal Functions (EOF) and Non-negative Matrix Factorization (NMF) to AOD retrieved by MODIS over China and surrounding areas. Results showed that EOF and NMF identified the important factors influencing AOD over China from different angles: natural dusts controlled the seasonal variation with contribution of 42.4%, and anthropogenic emissions have larger contribution to AOD magnitude. To better observe the interannual variation of different sources, we removed seasonal cycles from original data and conducted EOF analysis on AOD monthly anomalies. Results showed that aerosols from anthropogenic sources had the greatest contribution (27%) to AOD anomaly variation and took an obvious downward trend, and natural dust was the second largest contributor with contribution of 17%. In the areas surrounding China, the eastward aerosol transport due to prevailing westerlies in spring significantly influenced the AOD variation over West Pacific with the largest contribution of 21%, whereas the aerosol transport from BTH region in winter had relative greater impact on the AOD magnitude. After removing seasonal cycles, biomass burning in South Asia became the most important influencing factor on AOD anomalies with contribution of 10%, as its interannual variability was largely affected by El Niño. Aerosol transport from BTH was the second largest contributor with contribution of 8% and showed a decreasing trend. This study showed that the downward trend of AOD over China since 2011 was dominated by aerosols from anthropogenic sources, which in a way confirmed the effectiveness of air pollution control policies.

[Spatiotemporal Distribution of Aerosol Optical Depth Based on Landsat Data in the Hinterland of the Guanzhong Basin and Its Relationship with Urbanization].

Aerosol optical depth (AOD) is one of the most fundamental optical properties of aerosols that characterizes the attenuation of light by aerosols and is an indicator of regional air pollution. Based on the blue band surface reflectance database from the MOD09A1 products for the period 2000-2019 and the ASTER spectral database, AOD was estimated from Landsat TM/OLI data using the deep blue algorithm (DB). Multi-year average/annual average and seasonal AOD values for the period 2000-2019 were then calculated to analyze the spatial characteristics and temporal variations of AOD using the empirical orthogonal function method (EOF). Furthermore, the impacts of urbanization on the spatio-temporal distribution of AOD were analyzed. The obtained results are summarized as follows:① The multi-year average AOD spatial distribution in the hinterland of the Guanzhong Basin was significantly affected by topography and human activities, with higher AOD values and variationsin areas of low altitude and high-intensity human activities compared to the surrounding mountains. Thus, changes in AOD in the study area are mainly affected by anthropogenic factors. AOD also showed significant seasonal variations, whereby spring (0.34) > summer (0.33) > autumn (0.23) > winter (0.12), and the largest regional differences occurred in summer; ② The annual average AOD (from 2000-2019) showed the trend of "increase-decrease-increase", and reached a maximum in 2005, with the high AOD area gradually moving to the south. The distribution of AOD values in spring and summer was relatively discrete, while it is in a low-value agglomeration state in winter; ③ Three main AOD spatial distribution modes were identified based on the EOF, which had cumulative contribution rate of 83.0%. The spatial distribution trend of AOD showed regional consistency, with feature vectors consistent with the altitude, thus reflecting the difference of AOD at different altitudes. Taking the Qinling Mountains as the dividing line, the AOD presented the "north-south" pattern, AOD showed a "north-south" pattern, reflecting the uniqueness of the regional development in the Guanzhong Basin compared to the southern Qinling Mountains. The "southeast-northwest" distribution pattern indicated that the AOD presented a reverse change trend between urban and non-urban; and ④ The results of correlation analysis between the AOD and urbanization revealed a positive correlation with permanent population density (R2=0.707, P<0.05), impervious surface density (R2=0.377, P<0.05), and industrial POI density (R2=0.727, P<0.5). These results are significant for improving the monitoring of air quality in the Guanzhong Basin and for the construction of an urban ecological environment.

Spatial-temporal characteristics of AIDS incidences in Mainland China.

OBJECT: Revealed the spatial-temporal patterns of acquired immune deficiency syndrome (AIDS) incidences in Mainland China. METHODS: Empirical orthogonal function (EOF) technique was applied to analyze the major spatial distribution modes and the temporal changes of AIDS incidences in Mainland China during 2002-2017. RESULTS: The annual average AIDS incidences increased from 0.06 per 100 000 in 2002 to 4.15 per 100 000 in 2017, with an annual average increase of 0.31 per 100 000. The southwest regions were high-incidence areas, as well as Xinjiang province in the northwest. There were two typical spatial modes. EOF 1 represented an isodirectional spatial pattern that the incidences were relatively high in general, and the fluctuation ranges were relatively high in the southwest and northeast. EOF 2 represented a reverse spatial pattern that the incidences were relatively high (or low) in Guangxi, Yunnan, Xinjiang, Shanghai, and Henan, yet were relatively low (or high) in the remaining regions. CONCLUSION: The AIDS incidences in Mainland China were relatively low during 2002-2010, yet were kept in a relatively high level since 2012. The prevention and control of AIDS need further development, especially in the southwest regions.

Evaluating climate and irrigation effects on spatiotemporal variabilities of regional groundwater in an arid area using EOFs.

Groundwater level is an important variable in the evolution process of ecological environment systems. However, spatiotemporal changes in groundwater level are attributed to the comprehensive influence of natural and anthropogenic activities. Therefore, understanding the major driving forces to changes in spatiotemporal patterns of groundwater level is essential for sustainable utilization of regional groundwater and sustaining healthy ecosystems, especially in arid areas. In this study, based on monthly observations of depth to groundwater table (DTGT) from 67 monitoring wells during 2001-2010 in the Yichang Irrigation Sub-district (YISD) of the Hetao Irrigation District (HID), which is located in Northwest China with an arid climate, the empirical orthogonal function (EOF) method was used to analyze the spatiotemporal variations of DTGT and the major driving forces. The EOF analysis results showed that the first two spatial structures (EOF1 and EOF2) of DTGT were found in this region, which explained over 65% and 8% of the spatial variation of DTGT, respectively. Meteorological factors (evaporation and temperature) were the first leading factors to drive the temporal pattern of the first expansion coefficient (EC1) corresponding to the EOF1 at intra-annual scale as well as inter-annual scale. Particularly, temperature controlled the EC1 pattern during the freezing period from December to March. Soil texture was shown to have good correlations with the spatial patterns of DTGT, although these correlations diminished when the depth exceeded 250 cm. This study provides strong evidence that the principal spatiotemporal variations of groundwater can be effectively extracted by the EOF method, thereby obtaining integrated views of the relationships between the groundwater system and meteorological and anthropogenic factors.

Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19.

The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM2.5 exposure and health risk during COVID-19, and its driving factors have been analyzed using spatiotemporal big data, including Tencent location-based services (LBS) data, place of interest (POI), and PM2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM2.5 concentration firstly. Then, population exposure and health risks of PM2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM2.5 pollution, the relationship between PM2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM2.5 concentrations distributed with a U-shape, i.e., with a decrease followed by an increase from January to December. In terms of spatial distribution, the PM2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM2.5 in the west is significantly lower than that in the Middle East. Urban gross domestic product (GDP) and urban green area are negatively correlated with PM2.5; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM2.5 pollution. In terms of reducing the health risks and PM2.5 pollution, several pointed suggestions to improve the status has been proposed.

Spatiotemporal decomposition and risk determinants of hand, foot and mouth disease in Henan, China.

Hand, foot and mouth disease (HFMD) remains an increasing public health concern. The spatiotemporal variation of HFMD can be represented from multiple-perspectives, and it may be driven by different dominant factors. In this study, the HFMD cases in children under the age of five years in each county in Henan province, China, from 2009 to 2013 were assessed to explore the integrative spatiotemporal patterns of HFMD and investigate their driving factors. The empirical orthogonal function was applied to identify representative spatiotemporal patterns. Then, GeoDetector was used to quantify the determinant powers of driving factors to the disease. The results indicated that the most prominent spatiotemporal pattern explained 56.21% of the total variance, presented in big cities, e.g. capital city and municipal districts. The dominant factors of this pattern were per capita gross domestic product and relative humidity, with determinant powers of 62% and 42%, respectively. The secondary spatiotemporal pattern explained 10.52% of the total variance, presented in the counties around big cities. The dominant factors for this pattern were the ratio of urban to rural population and precipitation, with determinant powers of 26% and 41%, respectively. These findings unveiled the key spatiotemporal features and their determinants related to the disease; this will be helpful in establishing accurate spatiotemporal preventing of HFMD.

Regional and seasonal variations of outdoor thermal comfort in China from 1966 to 2016.

The optimized design of outdoor environment is of utmost importance due to its impact on human health, urban livability and energy consumption inside buildings. The outdoor thermal comfort and its spatiotemporal variations were assessed using Universal Thermal Climate Index (UTCI). Annual and seasonal UTCI were calculated using the daily dataset collected from 591 stations in China between 1966 and 2016. A REOF-cluster-EOF hybrid model was developed to optimize regionalization and assess regional-scale variations for UTCI. The results showed the following: (1) UTCI values decreased due to the increase of the latitude in China except for the Qinghai-Tibet Plateau. 69.5% of the total area of China experienced "no thermal stress" conditions in summer, whereas it was only 7.7% in winter. Additionally, the outdoor environment in summer had a wider "thermal comfort zone" than that in other seasons. (2) China was divided into a small number of regions with coherent UTCI changes using REOF analysis and K-means clustering algorithm. Eight homogeneous regions were obtained for annual UTCI. From spring to winter, the numbers of homogeneous regions were eight, nine, ten and seven, respectively. (3) Using EOF analysis, dominant patterns of UTCI in each region were extracted by the first two EOF modes, which accounted for >60% of the total variance. In the first mode, the significant upward trends of UTCI were detected in each region, suggesting the stronger outdoor heat stress. In the second mode, UTCI showed fluctuation between the cold and warm periods with different turning points between regions. Overall, the outdoor thermal comfort seemed to be improved more in high-latitude regions than that in low-latitude regions.