Modifying temperature-related cardiovascular mortality through green-blue space exposure.

Green-blue spaces (GBS) are pivotal in mitigating thermal discomfort. However, their management lacks guidelines rooted in epidemiological evidence for specific planning and design. Here we show how various GBS types modify the link between non-optimal temperatures and cardiovascular mortality across different thermal extremes. We merged fine-scale population density and GBS data to create novel GBS exposure index. A case time series approach was employed to analyse temperature-cardiovascular mortality association and the effect modifications of type-specific GBSs across 1085 subdistricts in south-eastern China. Our findings indicate that both green and blue spaces may significantly reduce high-temperature-related cardiovascular mortality risks (e.g., for low (5%) vs. high (95%) level of overall green spaces at 99th vs. minimum mortality temperature (MMT), Ratio of relative risk (RRR) = 1.14 (95% CI: 1.07, 1.21); for overall blue spaces, RRR = 1.20 (95% CI: 1.12, 1.29)), while specific blue space types offer protection against cold temperatures (e.g., for the rivers at 1st vs MMT, RRR = 1.17 (95% CI: 1.07, 1.28)). Notably, forests, parks, nature reserves, street greenery, and lakes are linked with lower heat-related cardiovascular mortality, whereas rivers and coasts mitigate cold-related cardiovascular mortality. Blue spaces provide greater benefits than green spaces. The severity of temperature extremes further amplifies GBS's protective effects. This study enhances our understanding of how type-specific GBS influences health risks associated with non-optimal temperatures, offering valuable insights for integrating GBS into climate adaptation strategies for maximal health benefits.

Short-term effects of fine particulate matter constituents on mortality considering the mortality displacement in Zhejiang province, China.

BACKGROUND: Evidence linking mortality and short-term exposure to particulate matter (PM2.5) constituents was sparse. The mortality displacement was often unconsidered and may induce incorrect risk estimation. OBJECTIVES: To assess the short-term effects of PM2.5 constituents on all-cause mortality considering the mortality displacement. METHODS: Daily data on all-cause mortality and PM2.5 constituents, including sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic matters (OM), and black carbon (BC), were collected from 2009 to 2020. The mortality effect of PM2.5 and its constituents was estimated using a distributed lag non-linear model. Stratified analyses were performed by age, sex, and season. RESULTS: Per interquartile range increases in SO42-, NO3-, NH4+, OM, and BC were associated with the 1.42% (95%CI: 0.98, 1.87), 3.76% (3.34, 4.16), 2.26% (1.70, 2.83), 2.36% (2.02, 2.70), and 1.26% (0.91, 1.61) increases in all-cause mortality, respectively. Mortality displacements were observed for PM2.5, SO42-, NH4+, OM, and BC, with their overall effects lasting for 7-15 days. Stratified analyses revealed a higher risk for old adults (>65 years) and females, with stronger effects in the cold season. CONCLUSIONS: Short-term exposures to PM2.5 constituents were positively associated with increased risks of mortality. The mortality displacement should be considered in future epidemiological studies on PM constituents. DATA AVAILABILITY: Data will be made available on request.

Inequalities of spatial primary healthcare accessibility in China.

A key step to the establishment of a tiered healthcare system is equitable access to basic primary healthcare services for all. However, no quantitative research on the national status quo of primary healthcare accessibility in China exists. We filled this gap by estimating spatial accessibility to primary healthcare centers (PHCs) and mapping its inequality across the mainland China. Four national datasets during 2015-2018, including administrative boundaries, residential communities, points-of-interest (including PHCs), and road networks, were collected to calculate the distance to the nearest PHC for each community. Five other national datasets including census, elevation, land use, vegetation, and nightlight, were collected to model 100m × 100 m population grids, based on which geographical modeling was used to calculate PHC accessibility of each community. Inequalities in PHC accessibility across China were described with concentration indices. About 44% of communities across China representing approximately 30% of the overall population had no access to PHCs within their 6-km catchment areas; about 78% of communities across China representing approximately 68.4% of the overall population had no access to PHCs within their 1.5-km catchment areas. Some municipalities/provinces like Shanghai, Beijing, Tianjin, Jiangsu, Shandong, and Zhejiang generally had higher proximity to the nearest PHCs, while others like Tibet, Guizhou, and Guangxi had lower proximity to the nearest PHCs. However, assuming similar basic service capacity across all PHCs, Shanghai, Tianjin, and Chongqing showed the lowest PHC accessibility due to high population density. Variations in PHC accessibility existed, with more inequalities observed in the north and northeastern provinces and less inequalities in southwestern and south-central provinces. This study demonstrates primary healthcare accessibility and inequality at province and city levels, and identifies communities with lower proximity and accessibility to PHCs in China. It would serve as a starting point to facilitate precise healthcare planning and preparedness for health emergencies in China.

Effect modifications of green space and blue space on heat-mortality association in Hong Kong, 2008-2017.

BACKGROUND: Despite emerging recognition of the benefits of green and blue spaces on human health, evidence for their effect modifications on heat-mortality associations is limited. We aimed to investigate the effect modifications of green and blue spaces on heat-mortality associations among different age and sex groups and at different heat levels. METHODS: Daily mortality and meteorological data from 2008 to 2017 in Hong Kong, China were collected. The Normalized Difference Vegetation Index and distance to coast were used as proxies for green and blue space exposure, respectively. Time-series analyses was performed using fitting generalized linear mixed models with an interaction term between heat and levels of exposure to either green or blue space. Age-, sex-, and heat level-stratified analyses were also conducted. RESULTS: With a 1 °C increase in temperature above the 90th percentile (29.61 °C), mortality increased by 5.7% (95% confidence interval [CI]: 1.6, 10.1%), 5.4% (1.4, 9.5%), and 4.6% (0.8, 8.9%) for low, medium and high levels of green space exposure, respectively, and by 7.5% (3.9, 11.2%) and 3.5% (0.3, 6.8%) for low and high levels of blue space exposure, respectively. Significant effect modifications of green and blue spaces were not observed for the whole population or any specific age and sex group, either at a moderate heat level or a heat level (Ps > 0.05). CONCLUSIONS: No significant effect modifications of green and blue spaces on heat-related mortality risk were observed in Hong Kong. These findings challenge the existing evidence on the prominent protective role of green and blue spaces in mitigating heat-related mortality risks.

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions.

Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.

Discovery of novel CA-4 analogs as dual inhibitors of tubulin polymerization and PD-1/PD-L1 interaction for cancer treatment.

A series of novel CA-4 analogs as dual inhibitors of tubulin polymerization and PD-1/PD-L1 were designed, synthesized and bio-evaluated. Among them, compound TP5 exhibited strongest inhibitory effects against five cancer cell lines with an IC50 value of 800 nM in HepG2 cells. In addition, mechanism studies revealed that TP5 could effectively inhibit tubulin polymerization, suppress HepG2 cells migration and colony formation, and cause cell arrest at G2/M phase and induce apoptosis. Furthermore, TP5 exhibited moderate anti-PD-1/PD-L1 activity with IC50 values of 48.76 µM in a homogenous time-resolved fluorescence (HTRF) assay. In vivo efficacy studies indicated that TP5 could significantly suppress tumor growth in an immune checkpoint humanized mouse model with a Tumor Growth Suppression (TGI) of 57.9% at 100 mg/kg without causing significant toxicity. Moreover, TP5 did not cause in vivo cardiotoxicity in BALB/c mice. These results suggest that the novel CA-4 analogs may serve as a starting point for developing more potent dual inhibitors of tubulin polymerization and PD-1/PD-L1.

Discovery of Novel and Highly Potent Resorcinol Dibenzyl Ether-Based PD-1/PD-L1 Inhibitors with Improved Drug-like and Pharmacokinetic Properties for Cancer Treatment.

A series of programmed cell death-1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors based on the resorcinol diphenyl ether scaffold were discovered by incorporating hydrophilic moieties into the side chain and converting into the corresponding hydrochloride salt. Among these compounds, P18 showed the highest inhibitory activity against PD-1/PD-L1 with an IC50 value of 9.1 nM in a homogeneous time-resolved fluorescence binding assay. Besides, P18 promoted HepG2 cell death dose dependently in a HepG2/PD-L1 and Jurkat/PD-1 coculture cell model. Further, P18 demonstrated significantly higher water solubility (17.61 mg/mL) and improved pharmacokinetics (e.g., t1/2 of ∼20 h and oral bioavailability of 12%) than the previous analogues. Moreover, P18 was highly effective in suppressing tumor growth in an immune checkpoint humanized mouse model without apparent toxicity. Collectively, these results suggest that compound P18 represents a promising PD-1/PD-L1 inhibitor worthy of further investigation as a potential anticancer agent.

Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data.

Rapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top-down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m-2. The highest AHF values were observed in some heavy industrial zones with value up to 415 W m-2. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China.

With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China's LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China's LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies.

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

Phytoplankton decline in the eastern North Pacific transition zone associated with atmospheric blocking.

Global climate change can significantly influence oceanic phytoplankton dynamics, and thus biogeochemical cycles and marine food webs. However, associative explanations based on the correlation between chlorophyll-a concentration (Chl-a) and climatic indices is inadequate to describe the mechanism of the connection between climate change, large-scale atmospheric dynamics, and phytoplankton variability. Here, by analyzing multiple satellite observations of Chl-a and atmospheric conditions from National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis datasets, we show that high-latitude atmospheric blocking events over Alaska are the primary drivers of the recent decline of Chl-a in the eastern North Pacific transition zone. These blocking events were associated with the persistence of large-scale atmosphere pressure fields that decreased westerly winds and southward Ekman transport over the subarctic ocean gyre. Reduced southward Ekman transport leads to reductions in nutrient availability to phytoplankton in the transition zone. The findings describe a previously unidentified climatic factor that contributed to the recent decline of phytoplankton in this region and propose a mechanism of the top-down teleconnection between the high-latitude atmospheric circulation anomalies and the subtropical oceanic primary productivity. The results also highlight the importance of understanding teleconnection among atmosphere-ocean interactions as a means to anticipate future climate change impacts on oceanic primary production.

Evidence for Urban-Rural Disparity in Temperature-Mortality Relationships in Zhejiang Province, China.

BACKGROUND: Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban-rural differences in the temperature impacts on health outcomes. OBJECTIVES: We investigated whether temperature-mortality relationships vary between urban and rural counties in China. METHODS: We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban-rural differences were explored using meta-regression with county-level characteristics. RESULTS: Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban-rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 [95% confidence interval (CI): 1.32, 1.62] associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban-rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types. CONCLUSIONS: Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure-response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban-rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban-rural disparity in mortality risks. https://doi.org/10.1289/EHP3556.

Spatiotemporal or temporal index to assess the association between temperature variability and mortality in China?

Epidemiological studies increasingly provide evidence about the adverse health effects of temperature variability (TV), which reflects short-term intra- and inter-day temperature change. However, calculation of TV only considers the temporal variability and lacks spatial variability. This study intends to investigate whether the lack of spatial variability in TV calculations has biased the health effect estimates. We collected daily data from the fine-gridded hourly temperatures and more than 2 million all-cause mortality counts in Zhejiang province in China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures based on records from multiple sites. This new index could be compared to the two typical temporal TV indices that are calculated based on the hourly temperatures from one-site and area-average records. The three types of TV are compared using a three-stage analytical approach: district-specific time series Poisson regression, meta-analysis, and calculation of attributable mortality fraction. We observe that both spatiotemporal and temporal TVs produce very similar TV-mortality associations, attributable mortality fractions, and model fits at the district level. For example, the mortality increase associated for every increase of 1 °C during 0-7 exposure days is 1.53% (95% CI: 1.31, 1.73) in spatiotemporal TV, whereas it is 1.48% (95% CI: 1.27, 1.68) and 1.45% (95% CI: 1.24, 1.67) in the one-site and area-average temporal TV, respectively. Thus, time series models using temporal TV index are equally good at estimating the associations between TV and mortality as spatiotemporal TV at the district level in population-based epidemiological studies in China. Epidemiological studies using temperature from one site or the averages of multiple sites in TV calculation will not bias the effect estimates of TV. Our study could provide an important guidance method for future TV-related research in China and even in other countries.

Temperature variability and mortality in rural and urban areas in Zhejiang province, China: An application of a spatiotemporal index.

BACKGROUND: Temperature variability (TV) is a potential trigger for death in urban areas, but there is little evidence of this in rural areas. In addition, a typical TV index only considers the temporal variability of temperature and ignores its spatial variability, which should be considered due to the effects of human mobility. Here this study aimed to 1) develop a novel spatiotemporal TV index accounting for human mobility; and 2) based on this index, explore the urban-rural differences in TV-mortality associations in China. METHODS: We collected daily data on fine-gridded hourly temperatures and >2 million deaths that occurred in Zhejiang province, China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures from multi-site records over the course of several exposure days. A three-stage analysis was performed to estimate the mortality risks and mortality burdens of TV. Stratified analyses were performed by cause-specific mortality, urban/rural district, age and gender. RESULTS: Significant associations were found between TV and all types of targeted diseases, age groups, and genders. Percentage increase in mortality associated with a 1 °C increase in TV at 0-7 exposure days were found to be higher for rural dwellers than urban dwellers in the warm season [for all-cause mortality, 2.07% (95% CI: 1.49%, 2.64%) vs. 1.16% (95%CI: 0.70%, 1.62%)]. An estimated all-cause mortality fraction of 5.33% was attributable to TV, with 4.99% in urban areas and 6.02% in rural areas. The elderly (aged 65+ years) and females were more sensitive to TV than young people and males, respectively. CONCLUSIONS: A spatiotemporal TV index was developed, considering both the temporal and spatial variability of temperatures. TV is an independent health risk factor. In China, rural areas generally suffer greater TV-related mortality risks than urban areas in the warm season. Our findings have important implications for developing area-, cause-, and group-specific adaptation strategies and emergency planning to reduce TV-related mortality.

Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model.

Remote sensing image products (e.g. brightness of nighttime lights and land cover/land use types) have been widely used to disaggregate census data to produce gridded population maps for large geographic areas. The advent of the geospatial big data revolution has created additional opportunities to map population distributions at fine resolutions with high accuracy. A considerable proportion of the geospatial data contains semantic information that indicates different categories of human activities occurring at exact geographic locations. Such information is often lacking in remote sensing data. In addition, the remarkable progress in machine learning provides toolkits for demographers to model complex nonlinear correlations between population and heterogeneous geographic covariates. In this study, a typical type of geospatial big data, points-of-interest (POIs), was combined with multi-source remote sensing data in a random forests model to disaggregate the 2010 county-level census population data to 100 × 100 m grids. Compared with the WorldPop population dataset, our population map showed higher accuracy. The root mean square error for population estimates in Beijing, Shanghai, Guangzhou, and Chongqing for this method and WorldPop were 27,829 and 34,193, respectively. The large under-allocation of the population in urban areas and over-allocation in rural areas in the WorldPop dataset was greatly reduced in this new population map. Apart from revealing the effectiveness of POIs in improving population mapping, this study promises the potential of geospatial big data for mapping other socioeconomic parameters in the future.

Mortality burden attributable to PM1 in Zhejiang province, China.

BACKGROUND: Limited evidence is available on the health effects of particulate matter with an aerodynamic diameter of <1 µm (PM1), mainly due to the lack of its ground measurement worldwide. OBJECTIVES: To identify and examine the mortality risks and mortality burdens associated with PM1, PM2.5, and PM10 in Zhejiang province, China. METHODS: We collected daily data regarding all-cause (stratified by age and gender), cardiovascular, stroke, respiratory, and chronic obstructive pulmonary disease (COPD) mortality, and PM1, PM2.5, and PM10, from 11 cities in Zhejiang province, China during 2013 and 2017. We used a quasi-Poisson regression model to estimate city-specific associations between mortality and PM concentrations. Then we used a random-effect meta-analysis to pool the provincial estimates. To show the mortality burdens of PM1, PM2.5, and PM10, we calculated the mortality fractions and deaths attributable to these PMs. RESULTS: Daily concentrations of PM1, PM2.5, and PM10 ranged between 0-199 µg/m3, 0-218 µg/m3, and 0-254 µg/m3, respectively; Mortality effects were significant in lag 0-2 days. The relative risks for all-cause mortality were 1.0064 (95% CI: 1.0034, 1.0094), 1.0061 (95% CI: 1.0034, 1.0089), and 1.0060 (95% CI: 1.0038, 1.0083) associated with a 10 µg/m3 increase in PM1, PM2.5, and PM10, respectively. Age- and gender-stratified analysis shows that elderly people (aged 65+) and females are more sensitive to PMs. The mortality fractions of all-cause mortality were estimated to be 2.39% (95% CI: 1.28, 3.48) attributable to PM1, 2.53% (95% CI: 1.42, 3.63) attributable to PM2.5, and 3.08% (95% CI: 1.95, 4.19) attributable to PM10. The ratios of attributable cause-specific deaths for PM1/PM2.5, PM1/PM10, and PM2.5/PM10 were higher than the ratios of their respective concentrations. CONCLUSIONS: PM1, PM2.5 and PM10 are risk factors of all-cause, cardiovascular, stroke, respiratory, and COPD mortality. PM1 accounts for the vast majority of short-term PM2.5- and PM10-induced mortality. Our analyses support the notion that smaller size fractions of PM have a more toxic mortality impacts, which suggests to develop strategies to prevent and control PM1 in China, such as to foster strict regulations for automobile and industrial emissions.

Spatially explicit assessment of heat health risk by using multi-sensor remote sensing images and socioeconomic data in Yangtze River Delta, China.

BACKGROUND: The increase in the frequency and intensity of extreme heat events, which are potentially associated with climate change in the near future, highlights the importance of heat health risk assessment, a significant reference for heat-related death reduction and intervention. However, a spatiotemporal mismatch exists between gridded heat hazard and human exposure in risk assessment, which hinders the identification of high-risk areas at finer scales. METHODS: A human settlement index integrated by nighttime light images, enhanced vegetation index, and digital elevation model data was utilized to assess the human exposure at high spatial resolution. Heat hazard and vulnerability index were generated by land surface temperature and demographic and socioeconomic census data, respectively. Spatially explicit assessment of heat health risk and its driving factors was conducted in the Yangtze River Delta (YRD), east China at 250 m pixel level. RESULTS: High-risk areas were mainly distributed in the urbanized areas of YRD, which were mostly driven by high human exposure and heat hazard index. In some less-urbanized cities and suburban and rural areas of mega-cities, the heat health risks are in second priority. The risks in some less-developed areas were high despite the low human exposure index because of high heat hazard and vulnerability index. CONCLUSIONS: This study illustrated a methodology for identifying high-risk areas by combining freely available multi-source data. Highly urbanized areas were considered hotspots of high heat health risks, which were largely driven by the increasing urban heat island effects and population density in urban areas. Repercussions of overheating were weakened due to the low social vulnerability in some central areas benefitting from the low proportion of sensitive population or the high level of socioeconomic development. By contrast, high social vulnerability intensifies heat health risks in some less-urbanized cities and suburban areas of mega-cities.

Spatially Explicit Mapping of Heat Health Risk Utilizing Environmental and Socioeconomic Data.

Extreme heat events, a leading cause of weather-related fatality worldwide, are expected to intensify, last longer, and occur more frequently in the near future. In heat health risk assessments, a spatiotemporal mismatch usually exists between hazard (heat stress) data and exposure (population distribution) data. Such mismatch is present because demographic data are generally updated every couple of years and unavailable at the subcensus unit level, which hinders the ability to diagnose human risks. In the present work, a human settlement index based on multisensor remote sensing data, including nighttime light, vegetation index, and digital elevation model data, was used for heat exposure assessment on a per-pixel basis. Moreover, the nighttime urban heat island effect was considered in heat hazard assessment. The heat-related health risk was spatially explicitly assessed and mapped at the 250 m × 250 m pixel level across Zhejiang Province in eastern China. The results showed that the accumulated heat risk estimates and the heat-related deaths were significantly correlated at the county level (Spearman's correlation coefficient = 0.76, P ≤ 0.01). Our analysis introduced a spatially specific methodology for the risk mapping of heat-related health outcomes, which is useful for decision support in preparation and mitigation of heat-related risk and potential adaptation.

[Exposure Level of Population and Economy in Zhejiang Province Considering the Background of PM2.5 in East China].

Using the satellite-derived Fine Particulate Matter (PM2.5) data of global high-precision products during 1998-2012, detailed characteristics of PM2.5 distribution over East China and its change are given, and the PM2.5 exposure levels for population and economy in Zhejiang is further analyzed in this context. The results show that the overall variability of the regional average value of PM2.5 concentration exhibits an increasing trend followed by a decrease during 1998-2012, which is consistent in both the East China and Zhejiang provinces; the turning point occurs around 2007-2009. In the initial years (1998-2000), annual average PM2.5 concentrations are below 50 µg·m-3 in the southern part of East China and eastern part of Shandong, while it is 50-75 µg·m-3 in other areas. During this period, 51.8% of the area in East China and 21.1% of the area in Zhejiang have PM2.5 concentrations larger than 35 µg·m-3 (Air quality standard value in China). The rising trend in PM2.5 concentration is extremely obvious in 1998-2009; the mean linear trend over East China is 2.58 µg·(m3·a)-1, while it is 1.43 µg·(m3·a)-1 over Zhejiang province. PM2.5 concentration reaches its maximum during 2007-2009. The area where PM2.5 concentration is larger than 35 µg·m-3 reaches 82.1% in East China and 65.9% in Zhejiang province. After that, the PM2.5 concentration begins to decrease in each region, and the average change trend in East China and Zhejiang are -1.75 µg·(m3·a)-1 and -1.58 µg·(m3·a)-1, respectively. The regional area in which PM2.5concentrations aregreater than 35 µg·m-3 displays a slight decline during 2010-2012, which suggests that the energy conservation and emission reduction plan proposed by the Chinese government and associated measures may play a vital role in improving air quality of East China. In 2010, the proportion of the population and GDP for which the PM2.5 exposure level exceeded 35 µg·m-3 were 74.0% and 70.8%, respectively, of which 38.1% of the population lives in an environment where annual average PM2.5 concentrations are more than 50 µg·m-3. In addition, 38.9% of the GDP is produced in an environment with annual average PM2.5 concentrations more than 50 µg·m-3.

Environmental consequences of rapid urbanization in zhejiang province, East china.

Since reforms carried out in the late 1970s, China has experienced unprecedented rates of urban growth. Remote sensing data and surface observational data are used to investigate the urbanization process and related environmental consequences, focusing on extreme heat events and air pollution, in Zhejiang Province (ZJP, East China). Examination of satellite-measured nighttime light data indicates rapid urbanization in ZJP during the past decade, initially forming three urban clusters. With rapid urban sprawl, a significant Urban Heat Island (UHI) effect has emerged. During extreme heat events in summer, the UHI effect significantly exacerbates nocturnal heat stress in highly urbanized areas. Taking a long-term view, urbanization also causes additional hot days and hot degree days in urban areas. Urbanization also imposes a heavy burden on local and regional air quality in ZJP. Degraded visibility and an increase in haze days are observed at most meteorological stations, especially in the three urban clusters. The results show that urbanization has led to serious environmental problems in ZJP, not only on the city scale, but also on the regional scale. Maintaining a balance between the continuing process of urbanization and environmental sustainability is a major issue facing the local government.