Summertime ozone (O₃) pollution has frequently occurred in the Beijing–Tianjin–Hebei (BTH) region, China, since 2013, resulting in detrimental impacts on human health and ecosystems. The contribution of weather shifts to O₃ concentration variability owing to climate change remains elusive. By combining regional air chemistry model simulations with near-surface observations, we found that anthropogenic emission changes contributed to approximately 23% of the increase in maximum daily 8-h average O₃ concentrations in the BTH region in June–July–August (JJA) 2017 (compared with that in 2013). With respect to the weather shift influence, the frequencies, durations, and magnitudes of O₃ exceedance were consistent with those of the heat wave events in the BTH region during JJA in 2013–2017. Intensified heat waves are a significant driver for worsening O₃ pollution. In particular, the prolonged duration of heat waves creates consecutive adverse weather conditions that cause O₃ accumulation and severe O₃ pollution. Our results suggest that the variability in extreme summer heat is closely related to the occurrence of high O₃ concentrations, which is a significant driver of deteriorating O₃ pollution.

Non-optimum ambient temperature has been associated with a variety of health outcomes in the elderly population. However, few studies have examined its adverse effects on neurocognitive function. In this study, we explored the temperature-cognition association in elderly women. We investigated 777 elderly women from the German SALIA cohort during the 2007–2010 follow-up. Cognitive function was evaluated using the CERAD-Plus test battery. Modelled data on daily weather conditions were assigned to the residential addresses. The temperature-cognition association over lag 0–10 days was estimated using multivariable regression with distributed lag non-linear model. The daily mean temperature ranged between −6.7 and 26.0 °C during the study period for the 777 participants. We observed an inverse U-shaped association in elderly women, with the optimum temperature (15.3 °C) located at the 68th percentile of the temperature range. The average z-score of global cognitive function declined by −0.31 (95%CI: 0.73, 0.11) for extreme cold (the 2.5th percentile of temperature range) and −0.92 (95%CI: 1.50, −0.33) for extreme heat (the 97.5th percentile of temperature range), in comparison to the optimum temperature. Episodic memory was more sensitive to heat exposure, while semantic memory and executive function were the two cognitive domains sensitive to cold exposure. Individuals living in an urban area and those with a low educational level were particularly sensitive to extreme heat. In summary, non-optimum temperature was inversely associated with cognitive function in elderly women, with the effect size for heat exposure particularly substantial. The strength of association varied by cognitive domains and individual characteristics.

The association between temperature and mortality has been widely reported. However, it remains largely unclear whether inflammation-related diseases, caused by excessive or inappropriate inflammatory reaction, may be affected by ambient temperature, particularly in low-income areas.To explore the association between ambient temperature and clinical visits for inflammation-related diseases in rural villages in the Ningxia Hui Autonomous Region, China, during 2012─2015.Daily data on inflammation-related diseases and weather conditions were collected from 258 villages in Haiyuan (161 villages) and Yanchi (97 villages) counties during 2012─2015. A Quasi-Poisson regression with distributed lag non-linear model was used to examine the association between temperature and clinical visits for inflammation-related diseases. Stratified analyses were performed by types of diseases including arthritis, gastroenteritis, and gynecological inflammations.During the study period, there were 724,788 and 288,965 clinical visits for inflammation-related diseases in Haiyuan and Yanchi, respectively. Both exposure to low (RR: 2.045, 95% CI: 1.690, 2.474) and high temperatures (RR: 1.244, 95% CI: 1.107, 1.399) were associated with increased risk of total inflammation-related visits in Haiyuan county. Low temperatures were associated with increased risks of all types of inflammation-related diseases in Yanchi county (RR: 4.344, 95% CI: 2.887, 6.535), while high temperatures only affected gastroenteritis (RR: 1.274, 95% CI: 1.040, 1.561). Moderate temperatures explained approximately 26% and 33% of clinical visits due to inflammation-related diseases in Haiyuan and Yanchi, respectively, with the burden attributable to cold exposure higher than hot exposure. The reference temperature values ranged from 17 to 19 in Haiyuan, and 12 to 14 in Yanchi for all types of clinical visits.Our findings add additional evidence for the adverse effect of suboptimal ambient temperature and provide useful information for public health programs targeting people living in rural villages.

Malaysia is a tropical country that is highly dependent on surface water for its raw water supply. Unfortunately, surface water is vulnerable to pollution, especially in developed and dense urban catchments. Therefore, in this study, a methodology was developed for an extensive temporal water quality index (WQI) and classification analysis, simulations of various pollutant discharge scenarios using QUAL2K software, and maps with NH₃–N as the core pollutant using an integrated QUAL2K-GIS. It was found that most of the water quality stations are categorized as Class III (slightly polluted to polluted). These stations are surrounded by residential areas, industries, workshops, restaurants and wet markets that contribute to the poor water quality levels. Additionally, low WQI values were reported in 2010 owing to development and agricultural activities. However, the WQI values improved during the wet season. High concentrations of NH₃–N were found in the basin, especially during dry weather conditions. Three scenarios were simulated, i.e. 10%, 50% and 70% of pollution discharge into Skudai river using a calibrated and validated QUAL2K model. Model performance was evaluated using the relative percentage difference. An inclusive graph showing the current conditions and pollution reduction scenarios with respect to the distance of Skudai river and its tributaries is developed to determine the WQI classification. Comprehensive water quality maps based on NH₃–N as the core pollutant are developed using integrated QUAL2K-GIS to illustrate the overall condition of the Skudai river. High NH₃–N in the Skudai River affects water treatment plant operations. Pollution control of more than 90% is required to improve the water quality classification to Class II. The methodology and analysis developed in this study can assist various stakeholders and authorities in identifying problematic areas and determining the required percentage of pollution reduction to improve the Skudai River water quality.

The new-generation geostationary satellites feature higher radiometric, spectral, and spatial resolutions, thereby making richer data available for the improvement of PM₂.₅ predictions. Various aerosol optical depth (AOD) data assimilation methods have been developed, but the accurate representation of the AOD-PM₂.₅ relationship remains challenging. Empirical statistical methods are effective in retrieving ground-level PM₂.₅, but few have been evaluated in terms of whether and to what extent they can help improve PM₂.₅ predictions. Therefore, an empirical and statistics-based scheme was developed for optimizing the estimation of the initial conditions (ICs) of aerosol in WRF-Chem (Weather Research and Forecasting/Chemistry) and for improving the PM₂.₅ predictions by integrating Himawari-8 data and ground observations. The proposed method was evaluated via two one-year experiments that were conducted in parallel over eastern China. The contribution of the satellite data to the model performance was evaluated via a 2-week control experiment. The results demonstrate that the proposed method improved the PM₂.₅ predictions throughout the year and mitigated the underestimation during pollution episodes. Spatially, the performance was highly correlated with the amount of valid data.

This study examined the spatial and temporal variations of severe particulate pollution days (SPPDs) in China by using observed PM₂.₅ concentrations during April 2013 to February 2018 from the Ministry of Environmental Protection of China. SPPDs were defined as those with observed daily mean PM₂.₅ concentrations larger than 150 μg m⁻³. Observations showed that northern China had the highest number of SPPDs during the studied period. Since 2015, the number of SPPDs in northwestern China is comparable to or even higher than that observed in Beijing-Tianjin-Hebei (BTH). The highest numbers of SPPDs observed within BTH and the Yangtze River Delta (YRD) were 122 (33), 95 (17), 57 (15), 78 (18), and 31 (25) days in 2013, 2014, 2015, 2016, and 2017, respectively, indicating a general decreasing trend as a result of emission reduction measures. SPPDs occurred mainly from November to February in BTH and in December and January in the YRD. The major circulation patterns associated with large-scale SPPDs were analyzed by using principal component analysis. Five typical synoptic weather patterns were identified for BTH. The most dominant weather type (a cold high centered over the Xinjiang and Mongolian regions) for BTH was also responsible for most of the SPPDs in the YRD. These results have important implications for emission control strategies during SPPDs. Emission control measures can be applied once the dominant circulation patterns have been predicted.

Previous studies have suggested that ambient temperature is associated with the mortality and morbidity of myocardial infarction (MI) although consistency among these investigations is lacking. We performed a meta-analysis to investigate the relationship between ambient temperature and MI. The PubMed, Web of Science, and China National Knowledge Infrastructure databases were searched back to August 31, 2017. The pooled estimates for different temperature exposures were calculated using a random-effects model. The Cochran's Q test and coefficient of inconsistency (I2) were used to evaluate heterogeneity, and the Egger's test was used to assess publication bias. The exposure-response relationship of temperature-MI mortality or hospitalization was modeled using random-effects meta-regression. A total of 30 papers were included in the review, and 23 studies were included in the meta-analysis. The pooled estimates for the relationship between temperature and the relative risk of MI hospitalization was 1.016 (95% confidence interval [CI]: 1.004–1.028) for a 1 °C increase and 1.014 (95% CI: 1.004–1.024) for a 1 °C decrease. The pooled estimate of MI mortality was 1.639 (95% CI: 1.087–2.470) for a heat wave. The heterogeneity was significant for heat exposure, cold exposure, and heat wave exposure. The Egger's test revealed potential publication bias for cold exposure and heat exposure, whereas there was no publication bias for heat wave exposure. An increase in latitude was associated with a decreased risk of MI hospitalization due to cold exposure. The association of heat exposure and heat wave were immediate, and the association of cold exposure were delayed. Consequently, cold exposure, heat exposure, and exposure to heat waves were associated with an increased risk of MI. Further research studies are required to understand the relationship between temperature and MI in different climate areas and extreme weather conditions.

Although a number of studies have assessed the occurrence of atmospheric polycyclic aromatic hydrocarbons (PAHs) in indoor environment, few studies have systemically examined the indoor-outdoor interplay of size-dependent particulate PAHs and potential health risk based on daily lifestyles. In the present study, size-dependent particle and gaseous samples were collected both indoors and outdoors within selected schools, offices and residences located in three districts of Guangzhou, China with different urbanization levels during the dry and wet weather seasons. Results from measurements of PAHs showed that higher total PAH concentrations occurred in residential areas than in other settings and in indoor than in outdoor environments. Compositional profiles and size distribution patterns of particle-bound PAHs were similar indoors and outdoors, predominated by 4-and 5-ring PAHs and the 0.56–1.0 μm particle fraction. Statistical analyses indicated that outdoor sources may have contributed to 38–99% and 62–100% of the variations for indoor particle-bound and gaseous PAH concentrations, respectively. Incremental life cancer risk (ILCR) from human exposure to indoor and outdoor PAHs based on different lifestyles followed the order of adults > children > adolescents > seniors. All average ILCR values for four age groups were below the lower limit of the Safe Acceptable Range (10−6). In addition, the ILCR value for adults (average: 7.2 × 10−7; 95% CI: 5.4 × 10−8‒2.5 × 10−6), estimated from outdoor air PAH levels with 24-h exposure time, was significantly higher than our assessment results (average: 5.9 × 10−7; 95% CI: 6.3 × 10−8‒1.9 × 10−6), suggesting the significance of assessing human inhalation exposure risks of indoor and outdoor PAHs in urban air based on daily lifestyles.

Seasonal variation of PM2.5 (Particulate Matter <2.5 μm) mass concentration simulated from WRF-Chem (Weather Research and Forecasting coupled with Chemistry) over Indian sub-continent are studied. The simulated PM2.5 are also compared with the observations during winter, pre-monsoon, monsoon and post-monsoon seasons of 2008. Higher value of simulated PM2.5 is observed during winter followed by post-monsoon, while lower values are found during monsoon. Indo-Gangetic Basin (IGB) exhibits high amount of PM2.5 (60− 200 μg m⁻³) throughout the year. The percentage differences between model simulated and observed PM2.5 are found higher (40− 60%) during winter, while lower (< 30%) during pre-monsoon and monsoon over most of the study locations. The weighted correlation coefficient between model simulated and observed PM2.5 is 0.81 at the significance of 98%. Associated RMSE (Root Mean Square Error) is 0.91 μg m⁻³. Large variability in vertically distributed PM2.5 are also found during pre-monsoon and monsoon. The study reveals that, model is able to capture the variabilities in spatial, seasonal and vertical distributions of PM2.5 over Indian region, however significant bias is observed in the model. PM2.5 mass concentrations are highest over West Bengal (82± 33 μg m⁻³) and the lowest in Jammu & Kashmir (14± 11 μg m⁻³). Annual mean of simulated PM2.5 mass over the Indian region is found to be 35± 9 μg m⁻³. Higher values of PM2.5 are found over the states, where the reported respiratory disorders are high. WRF-Chem simulated PM2.5 mass concentration gives a clear perspective of seasonal and spatial distribution of fine aerosols over the Indian region. The outcomes of the study have significant impacts on environment, human health and climate.

The spatiotemporal variability of ambient volatile organic compounds (VOCs) in Tehran, Iran, is not well understood. Here we present the design, methods, and results of the Tehran Study of Exposure Prediction for Environmental Health Research (Tehran SEPEHR) on ambient concentrations of benzene, toluene, ethylbenzene, p-xylene, m-xylene, and o-xylene (BTEX). To date, this is the largest study of its kind in a low- and middle-income country and one of the largest globally. We measured BTEX concentrations at five reference sites and 174 distributed sites identified by a cluster analysis method. Samples were taken over 25 2-weeks at five reference sites (to be used for temporal adjustments) and over three 2-week campaigns in summer, winter, and spring at 174 distributed sites. The annual median (25th–75th percentile) for benzene, the most carcinogenic of the BTEX species, was 7.8 (6.3–9.9) μg/m3, and was higher than the national and European Union air quality standard of 5 μg/m3 at approximately 90% of the measured sites. The estimated annual mean concentrations of BTEX were spatially highly correlated for all pollutants (Spearman rank coefficient 0.81–0.98). In general, concentrations and spatial variability were highest during the summer months, most likely due to fuel evaporation in hot weather. The annual median of benzene and total BTEX across the 35 sites in the Tehran regulatory monitoring network (7.7 and 56.8 μg/m3, respectively) did a reasonable job of approximating the 144 city-wide sites (7.9 and 58.7 μg/m3, respectively). The annual median concentrations of benzene and total BTEX within 300 m of gas stations were 9.1 and 67.3 μg/m3, respectively, and were higher than sites outside this buffer. We further found that airport did not affect annual BTEX concentrations of sites within 1 km. Overall, the observed ambient concentrations of toxic VOCs are a public health concern in Tehran.