Air pollution and health outcomes: Evidence from Black Saturday Bushfires in Australia

Environmental regulation and mental well-being: Evidence from China's air pollution prevention and control action plan

This study investigates how enhanced environmental regulation can improve individuals' mental well-being, focusing on the impact of China's so far most stringent air pollution control policy, the 2013 Air Pollution Prevention and Control Action Plan (APPCAP). Exploiting variations in timing and regions of the implementation of the policy, we find that the APPCAP has significantly improved people's mental well-being. We test several potential socio-economic channels including reduced air pollution, enhanced environmental awareness, improved physical health, and decreased physical activities during periods of heavy pollution, through which environmental regulation may affect mental well-being. Our findings highlight that increased public awareness concerning air pollution plays an important role in the health effects of environmental regulations. The positive effects of environmental regulation on mental well-being are particularly pronounced among individuals aged 45-59 and for those with higher-than-average income or education. We do not find that the positive effects of environmental regulation differ by gender. We further show that the 4-week prevalence of mental/neurological disease dropped significantly, by about 0.38 percentage points, after the implementation of the APPCAP, reaffirming significant mental health benefits from the environmental regulation.

Air quality historical correlation model based on time series

Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model-the Gaussian Hidden Markov Model (GHMM)-using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching method and the Fixed Training Set Length method are utilized. Both direct and indirect prediction modes are then used to predict the AQI in the Zhangdian District. Experimental results indicate that the improved GHMM with the indirect mode provides higher accuracy and more stable state estimation results (MAE = 13.59, RMSE = 17.59, mean forecasted value = 117.94). Finally, the air quality historical correlation model is integrated with the air quality meteorological correlation model from a previous study, further improving prediction accuracy (MAE = 11.59, RMSE = 14.87, mean forecasted value = 120.88). This study demonstrates that the GHMM's strong ability to analyze temporal features significantly enhances the accuracy and stability of air quality predictions. The integration of the air quality historical correlation model with the air quality meteorological correlation model from a previous study leverages the strengths of each sub-model in handling different feature groups, leading to even more accurate predictions.

Measuring long-term exposure to wildfire PM2.5 in California: Time-varying inequities in environmental burden

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 < 5 μg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 μg/m3 wildfire PM2.5); and 5) mean annual wildfire PM2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM2.5 exposure for several racial/ethnic groups.

Impact of ecological reserves on the local residents' health: Evidence from a natural experiment in China

The potential influence of natural ecosystems on human health has been widely acknowledged globally. Nevertheless, the causality of such a correlation among the middle-aged and older populations in developing countries awaits further investigations. This study aims to understand how a specific natural ecosystem change, namely the establishment of ecological reserves, improves the physical and mental health of middle-aged and older residents in China. Two batches of national key eco-function zones (NKEFZs) in 2011 and 2016 are selected as a quasi-experiment; and a total of 128,755 middle-aged and older residents from a combined data set from the China Family Panel Studies (CFPS) and China Health and Retirement Longitudinal Studies (CHARLS) from 2010 to 2020. A difference-in-differences method was used to identify the causal effects of the natural ecosystem improvement on human health outcomes. The results indicate significant and sustained improvements in the physical health of local middle-aged and elderly residents following the implementation of the NKEFZs policy. Notably, ecological reserves with a water conservation function and those in Karst area have the most salient effects on physical health. Furthermore, this study shows that the creation of ecological reserves improves the mental health of middle-aged and older residents, with the effect varying based on changes in physical health. This study provides new insights into the positive impact of natural ecosystem improvement on human health outcomes, in particular physical and mental health.

Long-term impacts of non-occupational wildfire exposure on human health: A systematic review

The intensity and frequency of wildfires is increasing globally. The systematic review of the current evidence on long-term impacts of non-occupational wildfire exposure on human health has not been performed yet. To provide a systematic review and identify potential knowledge gaps in the current evidence of long-term impacts of non-occupational exposure to wildfire smoke and/or wildfire impacts on human health. We conducted a systematic search of the literature via MEDLINE, Embase and Scopus from the database inception to July 05, 2022. References from the included studies and relevant reviews were also considered. The Newcastle-Ottawa Scale (NOS) and a validated quality assessment framework were used to evaluate the quality of observational studies. Study results were synthesized descriptively. A total of 36 studies were included in our systematic review. Most studies were from developed countries (11 in Australia, 9 in Canada, 7 in the United States). Studies predominantly focused on mental health (21 studies, 58.33%), while evidence on long-term impacts of wildfire exposure on health outcomes other than mental health is limited. Current evidence indicated that long-term impacts of non-occupational wildfire exposure were associated with mortality (COVID-19 mortality, cardiovascular disease mortality and acute myocardial disease mortality), morbidity (mainly respiratory diseases), mental health disorders (mainly posttraumatic stress disorder), shorter height of children, reduced lung function and poorer general health status. However, no significant associations were observed for long-term impacts of wildfire exposure on child mortality and respiratory hospitalizations. The population-based high-quality evidence with quantitative analysis on this topic is still limited. Future well-designed studies considering extensive wildfire smoke air pollutants (e.g., particulate matter, ozone, nitrogen oxides) and estimating risk coefficient values for extensive health outcomes (e.g., mortality, morbidity) are warranted to fill current knowledge gaps.