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This study performed an input-output structural decomposition analysis on changes in COD, ammonia nitrogen, SO 2 , NOx, soot and dust, industrial solid waste, and CO 2 emission multipliers for 41 final products over the period 2007–2012 in China. The results show that during the examined period, emission multipliers were, in general, decreasing. The main driver of this was technical effects. The effects that made a significant contribution were concentrated in eight sectors: coal mining and washing; metals mining and quarrying; food and tobacco products; paper printing manufacturing; the chemical industry; non-metallic mineral products; metal smelting and rolling processing; and electricity, heat production, and supply. Moreover, the technical effects presented an obvious spillover. Although the contribution of the structural effects was far less than the technical ones, there were still some structural adjustments that led to significant synergistic mitigation. For example, the decrease in the direct demand of the agriculture products, electricity, and heat for food and tobacco products commonly reduced SO 2 , NOx, and CO 2 . In addition, four technical effects and most of the structural effects with high efficiency made small contributions. More than one third of the structural effects that showed obvious contributions played a positive role. [ABSTRACT FROM AUTHOR]

Background Understanding the health consequences of continuously rising temperatures—as is projected for China—is important in terms of developing heat-health adaptation and intervention programs. This study aimed to examine the association between mortality and daily maximum (T max ), mean (T mean ), and minimum (T min ) temperatures in warmer months; to explore threshold temperatures; and to identify optimal heat indicators and vulnerable populations. Methods Daily data on temperature and mortality were obtained for the period 2007–2013. Heat thresholds for condition-specific mortality were estimated using an observed/expected analysis. We used a generalised additive model with a quasi-Poisson distribution to examine the association between mortality and T max /T min /T mean values higher than the threshold values, after adjustment for covariates. Results T max /T mean /T min thresholds were 32/28/24 °C for non-accidental deaths; 32/28/24 °C for cardiovascular deaths; 35/31/26 °C for respiratory deaths; and 34/31/28 °C for diabetes-related deaths. For each 1 °C increase in T max /T mean /T min above the threshold, the mortality risk of non-accidental-, cardiovascular-, respiratory, and diabetes-related death increased by 2.8/5.3/4.8%, 4.1/7.2/6.6%, 6.6/25.3/14.7%, and 13.3/30.5/47.6%, respectively. Thresholds for mortality differed according to health condition when stratified by sex, age, and education level. For non-accidental deaths, effects were significant in individuals aged ≥65 years (relative risk=1.038, 95% confidence interval: 1.026–1.050), but not for those ≤64 years. For most outcomes, women and people ≥65 years were more vulnerable. Conclusion High temperature significantly increases the risk of mortality in the population of Jinan, China. Climate change with rising temperatures may bring about the situation worse. Public health programs should be improved and implemented to prevent and reduce health risks during hot days, especially for the identified vulnerable groups. [ABSTRACT FROM AUTHOR]

Exposure to particulate matter (PM) could increase both susceptibility to SARS-CoV-2 infection and severity of COVID-19 disease. Prior studies investigating associations between PM and COVID-19 morbidity have only considered PM 2.5 or PM 10 , rather than PM 1. We investigated the associations between daily-diagnosed COVID-19 morbidity and average exposures to ambient PM 1 starting at 0 through 21 days before the day of diagnosis in 12 cities in China using a two-step analysis: a time-series quasi-Poisson analysis to analyze the associations in each city; and then a meta-analysis to estimate the overall association. Diagnosed morbidities and PM 1 data were obtained from National Health Commission in China and China Meteorological Administration, respectively. We found association between short-term exposures to ambient PM 1 with COVID-19 morbidity was significantly positive, and larger than the associations with PM 2.5 and PM 10. Percent increases in daily-diagnosed COVID-19 morbidity per IQR/10 PM 1 for different moving averages ranged from 1.50% (−1.20%, 4.30%) to 241% (95%CI: 80.7%, 545%), with largest values for exposure windows starting at 17 days before diagnosis. Our results indicate that smaller particles are more highly associated with COVID-19 morbidity, and most of the effects from PM 2.5 and PM 10 on COVID-19 may be primarily due to the PM 1. This study will be helpful for implementing measures and policies to control the spread of COVID-19. • The first attempt to examine the association between PM1 and COVID-19 morbidity. • Short-term exposure to ambient PM1 is associated with COVID-19 morbidity. • Smaller particles are more highly associated with COVID-19 morbidity. [ABSTRACT FROM AUTHOR]

There is no epidemiological evidence on the effects of maternal exposure to ambient particulate matter 10 μm or less in diameter (PM 10) and anencephaly risk in offspring. We conducted a population-based case-control study in Liaoning Province, China. The case group consisted of 663 cases with anencephaly and the control group consisted of 7950 healthy infants from the Maternal and Child Health Certificate Registry of Liaoning Province that were born between 2010 and 2015. Daily PM 10 concentrations were obtained from 77 monitoring stations located within the study area. A multivariable logistic regression model was established to calculate the adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Maternal PM 10 exposure was significantly associated with an increased risk of anencephaly at three months before conception (highest versus lowest tertile: OR = 1.74, 95% CI: 1.29–2.34; per 10 μg/m3 increment: OR = 1.13, 95% CI: 1.06–1.20) and three months after conception (highest versus lowest tertile: OR = 1.93, 95% CI: 1.44–2.60; per 10 μg/m3 increment: OR = 1.01, 95% CI: 0.95–1.08). The evaluation of shorter exposure windows revealed similar associations for PM 10 exposure from the third month before pregnancy to the third month after pregnancy. Maternal PM 10 exposure is positively associated with anencephaly risk during the critical period of neural system development. • There is no epidemiological evidence of maternal exposure to PM 10 and anencephaly risk. • Maternal exposure to PM 10 before and after pregnancy increases anencephaly risk. • Environmental health policies and actions should be adopted to reduce PM 10 emissions. [ABSTRACT FROM AUTHOR]

Climate change may lead to emerging and re-emerging infectious diseases and pose public health challenges to human health and the already overloaded healthcare system. It is therefore important to review current knowledge and identify further directions in China, the largest developing country in the world. A comprehensive literature review was conducted to examine the relationship between climate variability and infectious disease transmission in China in the new millennium. Literature was identified using the following MeSH terms and keywords: climatic variables [temperature, precipitation, rainfall, humidity, etc.] and infectious disease [viral, bacterial and parasitic diseases]. Fifty-eight articles published from January 1, 2000 to May 30, 2018 were included in the final analysis, including bacterial diarrhea, dengue, malaria, Japanese encephalitis, HFRS, HFMD, Schistosomiasis. Each 1 °C rise may lead to 3.6%–14.8% increase in the incidence of bacillary dysentery disease in south China. A 1 °C rise was corresponded to an increase of 1.8%–5.9% in the weekly notified HFMD cases in west China. Each 1 °C rise of temperature, 1％ rise in relative humidity and one hour rise in sunshine led to an increase of 0.90%, 3.99% and 0.68% in the monthly malaria cases, respectively. Climate change with the increased temperature and irregular patterns of rainfall may affect the pathogen reproduction rate, their spread and geographical distribution, change human behavior and influence the ecology of vectors, and increase the rate of disease transmission in different regions of China. Exploring relevant adaptation strategies and the health burden of climate change will assist public health authorities to develop an early warning system and protect China's population health, especially in the new 1.5 °C scenario of the newly released IPCC special report. • Many infectious diseases such as malaria and dengue fever are mostly affected by climate variability and climate change. • The impact of climate change on infectious disease transmission may be more prevalent in southern cities of China. • More infectious studies with multiple-locations and a long study period as well as adaptation strategies are required. [ABSTRACT FROM AUTHOR]

High PM 2.5 concentration threats ecosystem functions but limited quantitative studies have recognized PM 2.5 pollution as an individual stressor in evaluating ecological risk. In this study, we applied a machine-learning-based simulation model incorporating full-coverage satellite-driven PM 2.5 dataset to estimate high-resolution ground PM 2.5 concentration for the Golden Triangle of Southern Fujian Province, China (GTSF) in 2030 under two Representative Concentration Pathways (RCPs). Based on the simulation output, the ecological risk's spatiotemporal change and the risk for different land cover types, which were caused by PM 2.5 pollution, were assessed. We found that the PM 2.5 levels and ecological risk in the GTSF under RCP 4.5 would be reduced while those under RCP 8.5 would continue to increase. The regions with the highest ecological risk under RCP 4.5 are the most urbanized and industrialized districts, while those with the highest ecological risk under RCP 8.5 are of the highest rate in urbanization and the greatest decrease in planetary potential layer height. For both base years and 2030 under two RCPs, the ecological risk on developed land is the highest, while that on the forest is the lowest. Our study can provide useful information for environmental policy risk assessment. • Model simulations captured spatio-temporal trends of PM 2.5 in 2030 under two RCPs. • We assessed the ecological risk of PM 2.5 considering regional disparities. • The variation of land cover, population and climate change will induce polarized ecological risk under two RCPs. • The developed land in the GTSF has the highest ecological risk and the forest the lowest. [ABSTRACT FROM AUTHOR]