Ambient carbon monoxide and daily mortality: a global time-series study in 337 cities

Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October-November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32-49% to surface CO concentration while crop residue burning contributes 27-44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53-77%) of which 10-28% is from the domestic sector and 14-55% is from the transport sector. Agricultural waste burning contributes about 15-30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56-76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions.

The Multi-Country Multi-City Collaborative Research Network: An international research consortium investigating environment, climate, and health

Research on the health risks of environmental factors and climate change requires epidemiological evidence on associated health risks at a global scale. Multi-center studies offer an excellent framework for this purpose, but they present various methodological and logistical problems. This contribution illustrates the experience of the Multi-Country Multi-City Collaborative Research Network, an international collaboration working on a global research program on the associations between environmental stressors, climate, and health in a multi-center setting. The article illustrates the collaborative scheme based on mutual contribution and data and method sharing, describes the collection of a huge multi-location database, summarizes published research findings and future plans, and discusses advantages and limitations. The Multi-Country Multi-City represents an example of a collaborative research framework that has greatly contributed to advance knowledge on the health impacts of climate change and other environmental factors and can be replicated to address other research questions across various research fields.

Spatiotemporal heterogeneity of the association between short-term exposure to carbon monoxide and COVID-19 incidence: A multistage time-series study in the continental United States

Background

Previous research has established carbon monoxide (CO) as a significant air pollutant contributing to coronavirus disease 2019 (COVID-19) transmission. The spatiotemporal heterogeneity in the relationship between short-duration CO exposure and COVID-19 incidence remain underexplored. Investigating such heterogeneity plays a crucial role in designing region-specific cost-effective public health policies, exploring the reasons for heterogeneity, and understanding the temporal trends in the association between CO and an emerging infectious disease such as COVID-19.

Methods

The 49 states of the continental United States (U.S.) were examined in this study. Initially, we developed time-series generalized additive models (GAMs) for each state to assess the preliminary correlation between daily COVID-19 cases and short-term CO exposure from April 1, 2020, to December 31, 2021. Subsequently, the correlations were compiled utilizing Leroux-prior-based conditional autoregression (LCAR) to achieve a smoothed spatial distribution. Finally, we integrated a time-varying component into the GAM and LCAR to analyze temporal correlations and illuminate the factors contributing to spatiotemporal heterogeneity.

Results

Our analysis revealed that, across the 49 states, a 10-ppb increase in CO concentration was associated with a 1.33 % (95%CI: 0.86%-1.81 %) increase in COVID-19 cases on average. Furthermore, spatial variability was noted, with weaker correlations observed in the central and southeastern regions, stronger associations in the northeastern regions, and negligible associations in the western regions. Temporally, the correlation was not significant from April 2020 to June 2021, but began to increase steadily thereafter until the end of 2021. Additionally, vaccination and temperature were determined to be potential causes contributing to the heterogeneity, indicating stronger positive associations in areas with higher vaccination rates and temperatures.

Conclusion

The findings of this study underscore the importance of monitoring CO pollution in the central and northeastern US, especially in the aftermath of the pandemic.

Tracing vulnerable communities to ambient air pollution exposure: A geodemographic and remote sensing approach

Most studies analyzing the effects of air pollution on disadvantaged populations use ground air quality measurements. However, ground stations are generally limited, with nearly 40% of countries having no official PM2.5 stations, not allowing air quality analysis for a significantly large share of the world's population. Furthermore, limited studies analyze community data from a geodemographic perspective, in other words, to delineate the sociodemographic profiles and geographically locate the socioeconomic groups more exposed to ambient air pollution. Therefore, a significant question arises: How can we trace vulnerable communities to air pollution in areas lacking air-quality ground data? Here, we propose a novel methodology to respond to this question. We use NO2, SO2, CO, and HCHO tropospheric column air-quality data from Sentinel-5P, a satellite that quantifies concentrations of atmospheric species from space operationally. We integrate them with census and environmental data and apply the local fuzzy geographically weighted clustering spatial machine learning method for segmentation analysis. Our findings for Bali, Indonesia, provide quantitative evidence for the benefits of this methodology in tracing and delineating the profiles of the communities most exposed to air pollution. For example, results show that communities with highly disadvantaged populations, such as unemployed (over 27.8%), low educated (over 27.9%), and children (over 22.1%) (mainly located around Bali's south and north coast touristic areas), exhibit very high values (over the 75th quartile) across the pollutants studied. The proposed method is reproducible easily, quickly, and at low cost, as it is based on freely available satellite data and not on costly ground station measurements. This will hopefully assist decision-makers in tracing the most vulnerable subpopulations, even in areas with inadequate air-quality monitoring networks, thus allowing local governments around the globe (even those that are financially weak) to achieve environmental justice and their sustainable development goals.

Household carbon monoxide (CO) concentrations in a large African city: An unquantified public health burden?

Carbon monoxide (CO) is a poisonous gas produced by incomplete combustion of carbon-based fuels that is linked to mortality and morbidity. Household air pollution from burning fuels on poorly ventilated stoves can lead to high concentrations of CO in homes. There are few datasets available on household concentrations of CO in urban areas of sub-Saharan African countries. CO was measured every minute over 24 h in a sample of homes in Nairobi, Kenya. Data on household characteristics were gathered by questionnaire. Metrics of exposure were summarised and analysis of temporal changes in concentration was performed. Continuous 24-h data were available from 138 homes. The mean (SD), median (IQR) and maximum 24-h CO concentration was 4.9 (6.4), 2.8 (1.0-6.3) and 44 ppm, respectively. 50% of homes had detectable CO concentrations for 847 min (14h07m) or longer during the 24-h period, and 9% of homes would have activated a CO-alarm operating to European specifications. An association between a metric of total CO exposure and self-reported exposure to vapours >15 h per week was identified, however this were not statistically significant after adjustment for the multiple comparisons performed. Mean concentrations were broadly similar in homes from a more affluent area and an informal settlement. A model of typical exposure suggests that cooking is likely to be responsible for approximately 60% of the CO exposure of Nairobi schoolchildren. Household CO concentrations are substantial in Nairobi, Kenya, despite most homes using gas or liquid fuels. Concentrations tend to be highest during the evening, probably associated with periods of cooking. Household air pollution from cooking is the main source of CO exposure of Nairobi schoolchildren. The public health impacts of long-term CO exposure in cities in sub-Saharan Africa may be considerable and should be studied further.

Association of cause-specific hospital admissions with high and low temperatures in Thailand: a nationwide time series study

Background

Non-optimum temperatures are associated with a considerable mortality burden. However, evidence of temperature with all-cause and cause-specific hospital admissions in tropical countries like Thailand is still limited.

Methods

Daily all-cause and cause-specific hospital admissions for outpatient and inpatient visits were collected from 77 provinces in Thailand from January 2013 to August 2019. A two-stage time-series approach was applied to assess the association between non-optimum temperatures and hospital admission. We first fitted the province-specific temperature-morbidity association and then obtained the national association in the second stage using a random-effects meta-analysis regression. The attributable fraction (AF) of hospital admissions with 95% empirical confidence interval (eCI) was calculated.

Findings

A total of 878,513,460 all-cause outpatient admissions and 32,616,600 all-cause inpatient admissions were included in this study. We observed a J-shaped relationship with the risk of hospital admissions increasing for both cold and hot temperatures. The overall AFs of all-cause hospital admissions due to non-optimum temperatures were 7.57% (95% eCI: 6.47%, 8.39%) for outpatient visits and 6.17% (95% eCI: 4.88%, 7.20%) for inpatient visits. Hot temperatures were responsible for most of the AFs of hospital admissions, with 6.71% (95% eCI: 5.80%, 7.41%) for outpatient visits and 4.50% (95% eCI: 3.62%, 5.19%) for inpatient visits. The burden of hospital admissions was greater in females and in children and adolescents (0-19 years). The fractions of hospital admissions attributable to non-optimum temperatures exhibited variation among disease categories and geographical areas.

Interpretation

The results indicate that low and high temperature has a significant impact on hospital admissions, especially among the females, and children and adolescents (0-19 years). The current investigation could provide evidence for policymakers to develop adaptation strategies and mitigate the adverse effects of climate change on public health in Thailand and other tropical countries.

Funding

National Research Council of Thailand (NRCT): E-Asia Joint Research Program: Climate change impact on natural and human systems (N33A650979).

Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study

BACKGROUND

Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects.

OBJECTIVES

We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality.

METHODS

We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates.

RESULTS

Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0-7 (0.9 °C). An IQR increase in inter-day TV0-7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0-7 and inter-day TV0-7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type.

CONCLUSIONS

Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

BACKGROUND

Ambient nitrogen dioxide (NO 2 ) and fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants.

OBJECTIVES

Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO 2 - and PM 2.5 -attributable health burdens, considering NO 2 -attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants.

METHODS

Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO 2 and PM 2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study.

RESULTS

Despite overall decreases in the public health damages associated with NO 2 and PM 2.5 , racial and ethnic relative disparities in NO 2 -attributable pediatric asthma and PM 2.5 -attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM 2.5 -attributable premature mortality and NO 2 -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM 2.5 -attributable premature mortality have increased by 40% and NO 2 -attributable pediatric asthma by 10%.

DISCUSSION

Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Carbon emissions from various natural gas end-use sectors for 31 Chinese provinces between 2017 and 2021

Natural gas (NG) is a low-carbon fuel that is becoming a crucial transitional energy in China for reducing carbon emissions. In this study, a life-cycle assessment was performed to correlate carbon emissions and NG consumption for different end uses in China. A bottom-up life-cycle assessment framework was combined with carbon emission coefficients to quantify NG consumption in 31 Chinese provinces between 2017 and 2021, as well as the carbon emissions (in carbon dioxide (CO2) equivalents, including CO2 and methane) released during NG production, transportation, and consumption. The carbon emission factors for different types of end-use consumption were considered. The assessment results indicate that both NG consumption and life-cycle carbon emissions from NG use have increased since 2017. Between 2017 and 2021, NG consumption in China increased from 260 to 370 billion cubic meters and life-cycle carbon emissions from NG increased by 39% (from 930 to 1292 Mt CO2). The carbon emissions released during NG production and transportation accounted for approximately 31% of NG life-cycle emissions. Considerable variations in NG life-cycle carbon emissions were identified across different provinces and sectors, highlighting the need for targeted efforts to reduce carbon emissions. The objective of this study was to provide useful insights into sustainability development of the NG industry in China for optimizing NG allocations to different end uses and maximizing the environmental and economic benefits of NG.

Air Pollution and Arrhythmias

Air pollution is commonly defined as the contamination of the air we breathe by any chemical, physical, or biological agent that is potentially threatening to human and ecosystem health. The common pollutants known to be disease-causing are particulate matter, ground-level ozone, sulphur dioxide, nitrogen dioxide, and carbon monoxide. Although the association between increasing concentrations of these pollutants and cardiovascular disease is now accepted, the association of air pollution and arrhythmias is less well established. In this review we provide an in-depth discussion of the association of acute and chronic air pollution exposure and arrhythmia incidence, morbidity, and mortality, and the purported pathophysiological mechanisms. Increases in concentrations of air pollutants have multiple proarrhythmic mechanisms including systemic inflammation (via increases in reactive oxygen species, tumour necrosis factor, and direct effects from translocated particulate matter), structural remodelling (via an increased risk of atherosclerosis and myocardial infarction or by affecting the cell-to-cell coupling and gap junction function), and mitochondrial and autonomic dysfunction. Furthermore, we describe the associations of air pollution and arrhythmias. There is a strong correlation of acute and chronic air pollutant exposure and the incidence of atrial fibrillation. Acute increases in air pollution increase the risk of emergency room visits and hospital admissions for atrial fibrillation and the risk of stroke and mortality in patients with atrial fibrillation. Similarly, there is a strong correlation of increases of air pollutants and the risk of ventricular arrhythmias, out-of-hospital cardiac arrest, and sudden cardiac death.

Disease specific air quality health index (AQHI) for spatiotemporal health risk assessment of multi-air pollutants

While significant reductions in certain air pollutant concentrations did not induce obvious mitigations of health risks, a shift from air quality management to health risk prevention and control might be necessary to protect public health. This study thus constructed an Air Quality Health Index (AQHI) for respiratory (Res-AQHI), cardiovascular (Car-AQHI), and allergic (Aller-AQHI) risk groups using mixed exposure under multi-air pollutants and portrayed their distribution and variation at multiple spatiotemporal scales using spatial analysis in GIS with the medical big data and air pollution remote sensing data by taking Hunan Province in China as a case. Results showed that the AQHIs constructed for specific health-risk groups could better express their risks than common AQHI and AQI. Moreover, based on the spatiotemporal association of health and environmental information, the allergic risk group in Hunan provided the highest health risk mainly affected by O₃. The following cardiovascular and respiratory risk groups can be significantly attributed to NO₂. Moreover, the spatiotemporal heterogeneity of AQHIs within regions was also evident. On the annual scale, the population in the air health risk hotspots for respiratory and cardiovascular risk decreased, while allergic risks increased. Meanwhile, on seasonal scale, the hotspots for respiratory and cardiovascular risks expanded significantly in winter while completely disappearing for allergic risk. These findings suggest that disease specific AQHIs effectively disclose the health effects of multi-air pollutants and their subsequently varied spatiotemporal distribution patterns.

It's electric! An environmental equity perspective on the lifecycle of our energy sources

Energy policy decisions are driven primarily by economic and reliability considerations, with limited consideration given to public health, environmental justice, and climate change. Moreover, epidemiologic studies relevant for public policy typically focus on immediate public health implications of activities related to energy procurement and generation, considering less so health equity or the longer-term health consequences of climate change attributable to an energy source. A more integrated, collective consideration of these three domains can provide more robust guidance to policymakers, communities, and individuals. Here, we illustrate how these domains can be evaluated with respect to natural gas as an energy source. Our process began with a detailed overview of all relevant steps in the process of extracting, producing, and consuming natural gas. We synthesized existing epidemiologic and complementary evidence of how these processes impact public health, environmental justice, and climate change. We conclude that, in certain domains, natural gas looks beneficial (e.g., economically for some), but when considered more expansively, through the life cycle of natural gas and joint lenses of public health, environmental justice, and climate change, natural gas is rendered an undesirable energy source in the United States. A holistic climate health equity framework can inform how we value and deploy different energy sources in the service of public health.

Short-Term Association between Sulfur Dioxide and Mortality: A Multicountry Analysis in 399 Cities

BACKGROUND

Epidemiological evidence on the health risks of sulfur dioxide (SO 2 ) is more limited compared with other pollutants, and doubts remain on several aspects, such as the form of the exposure-response relationship, the potential role of copollutants, as well as the actual risk at low concentrations and possible temporal variation in risks.

OBJECTIVES

Our aim was to assess the short-term association between exposure to SO 2 and daily mortality in a large multilocation data set, using advanced study designs and statistical techniques.

METHODS

The analysis included 43,729,018 deaths that occurred in 399 cities within 23 countries between 1980 and 2018. A two-stage design was applied to assess the association between the daily concentration of SO 2 and mortality counts, including first-stage time-series regressions and second-stage multilevel random-effect meta-analyses. Secondary analyses assessed the exposure-response shape and the lag structure using spline terms and distributed lag models, respectively, and temporal variations in risk using a longitudinal meta-regression. Bi-pollutant models were applied to examine confounding effects of particulate matter with an aerodynamic diameter of ≤ 10 μ m (PM 10 ) and 2.5 μ m (PM 2.5 ), ozone, nitrogen dioxide, and carbon monoxide. Associations were reported as relative risks (RRs) and fractions of excess deaths.

RESULTS

The average daily concentration of SO 2 across the 399 cities was 11 . 7  μ g / m 3 , with 4.7% of days above the World Health Organization (WHO) guideline limit (40   μ g / m 3 , 24-h average), although the exceedances occurred predominantly in specific locations. Exposure levels decreased considerably during the study period, from an average concentration of 19.0   μ g / m 3 in 1980-1989 to 6.3   μ g / m 3 in 2010-2018. For all locations combined, a 10 - μ g / m 3 increase in daily SO 2 was associated with an RR of mortality of 1.0045 [95% confidence interval (CI): 1.0019, 1.0070], with the risk being stable over time but with substantial between-country heterogeneity. Short-term exposure to SO 2 was associated with an excess mortality fraction of 0.50% [95% empirical CI (eCI): 0.42%, 0.57%] in the 399 cities, although decreasing from 0.74% (0.61%, 0.85%) in 1980-1989 to 0.37% (0.27%, 0.47%) in 2010-2018. There was some evidence of nonlinearity, with a steep exposure-response relationship at low concentrations and the risk attenuating at higher levels. The relevant lag window was 0-3 d. Significant positive associations remained after controlling for other pollutants.

DISCUSSION

The analysis revealed independent mortality risks associated with short-term exposure to SO 2 , with no evidence of a threshold. Levels below the current WHO guidelines for 24-h averages were still associated with substantial excess mortality, indicating the potential benefits of stricter air quality standards. https://doi.org/10.1289/EHP11112.

A parent-school initiative to assess and predict air quality around a heavily trafficked school

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM₁, PM_2.5, PM₁₀), gaseous pollutants (NO₂, O₃ and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM_2.5 was around three-times the World Health Organisation limit. PM_2.5 and NO₂ exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM_2.5 and NO₂, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO₂ dataset that performed moderately (R², 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.

Selective detection of trace carbon monoxide at room temperature based on CuO nanosheets exposed to (111) crystal facets

In recent years, carbon monoxide (CO) intoxication incidents occur frequently, and the sensitive detection of CO is particularly significant. At present, most reported carbon monoxide (CO) sensors meet the disadvantage of high working temperature. It is always a challenge to realize the sensitive detection of carbon monoxide at room temperature. In this study, CuO nanosheets exposed more (111) active crystal facets and oxygen vacancy defects were synthesized by a simple and environmentally friendly one-step hydrothermal method. The sensor has good comprehensive gas sensing performance, compared with other sensors that can detect CO at room temperature. The response value to 100 ppm CO at room temperature is as high as 39.6. In addition, it also has excellent selectivity, low detection limit (100 ppb), good reproducibility, moisture resistance and long-term stability (60 days). This excellent gas sensing performance is attributed to the special structural characteristics of 2D materials and the synergistic effect of more active crystal facets exposed on the crystal surface and oxygen vacancy defects. Therefore, it is expected to become a promising sensitive material for rapid and accurate detection of trace CO gas under low energy consumption, reduce the risk of poisoning, and then effectively protect human life safety.

Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions

Human health is linked to climatic factors in complex ways, and climate change can have profound direct and indirect impacts on the health status of any given region. Susceptibility to climate change is modulated by biological, ecological and socio-political factors such as age, gender, geographic location, socio-economic status, occupation, health status and housing conditions, among other. In the Eastern Mediterranean and Middle East (EMME), climatic factors known to affect human health include extreme heat, water shortages and air pollution. Furthermore, the epidemiology of vector-borne diseases (VBDs) and the health consequences of population displacement are also influenced by climate change in this region. To inform future policies for adaptation and mitigation measures, and based on an extensive review of the available knowledge, we recommend several research priorities for the region. These include the generation of more empirical evidence on exposure-response functions involving climate change and specific health outcomes, the development of appropriate methodologies to evaluate the physical and psychological effects of climate change on vulnerable populations, determining how climate change alters the ecological determinants of human health, improving our understanding of the effects of long-term exposure to heat stress and air pollution, and evaluating the interactions between adaptation and mitigation strategies. Because national boundaries do not limit most climate-related factors expected to impact human health, we propose that adaptation/mitigation policies must have a regional scope, and therefore require collaborative efforts among EMME nations. Policy suggestions include a decisive region-wide decarbonisation, the integration of environmentally driven morbidity and mortality data throughout the region, advancing the development and widespread use of affordable technologies for the production and management of drinking water by non-traditional means, the development of comprehensive strategies to improve the health status of displaced populations, and fostering regional networks for monitoring and controlling the spread of infectious diseases and disease vectors.

Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network

In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).

Environmental variable importance for under-five mortality in Malaysia: A random forest approach

BACKGROUND

Environmental factors have been associated with adverse health effects in epidemiological studies. The main exposure variable is usually determined via prior knowledge or statistical methods. It may be challenging when evidence is scarce to support prior knowledge, or to address collinearity issues using statistical methods. This study aimed to investigate the importance level of environmental variables for the under-five mortality in Malaysia via random forest approach.

METHOD

We applied a conditional permutation importance via a random forest (CPI-RF) approach to evaluate the relative importance of the weather- and air pollution-related environmental factors on daily under-five mortality in Malaysia. This study spanned from January 1, 2014 to December 31, 2016. In data preparation, deviation mortality counts were derived through a generalized additive model, adjusting for long-term trend and seasonality. Analyses were conducted considering mortality causes (all-cause, natural-cause, or external-cause) and data structures (continuous, categorical, or all types [i.e., include all variables of continuous type and all variables of categorical type]). The main analysis comprised of two stages. In Stage 1, Boruta selection was applied for preliminary screening to remove highly unimportant variables. In Stage 2, the retained variables from Boruta were used in the CPI-RF analysis. The final importance value was obtained as an average value from a 10-fold cross-validation.

RESULT

Some heat-related variables (maximum temperature, heat wave), temperature variability, and haze-related variables (PM10, PM10-derived haze index, PM10- and fire-derived haze index, fire hotspot) were among the prominent variables associated with under-five mortality in Malaysia. The important variables were consistent for all- and natural-cause mortality and sensitivity analyses. However, different most important variables were observed between natural- and external-cause under-five mortality.

CONCLUSION

Heat-related variables, temperature variability, and haze-related variables were consistently prominent for all- and natural-cause under-five mortalities, but not for external-cause.

Short-Term Exposure to Ambient Air Pollution and Hospital Admissions for Sequelae of Stroke in Chinese Older Adults

Extensive evidence suggests that ambient air pollution contributes to a higher risk of hospital admissions for cerebrovascular diseases; however, its association with admissions for sequelae of stroke remains unclear. A time-stratified case-crossover study was conducted among 31,810 older adults who were admitted to hospital for sequelae of stroke in Guangzhou, China during 2016-2019. For each subject, daily residential exposure to fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) was extracted from a validated grid data set. Conditional logistic regression models were used for exposure-response analyses. In single-pollutant models, each interquartile range (IQR) increase of lag 04-day exposure to CO (IQR: 0.25 mg/m3) and lag 3-day exposure to O3 (69.6 μg/m3) was significantly associated with a 4.53% (95% confidence interval: 1.67%, 7.47%) and 5.63% (1.92%, 9.48%) increase in odds of hospital admissions for sequelae of stroke, respectively. These associations did not significantly vary across age or sex. With further adjustment for each of the other pollutants in 2-pollutant models, the association for CO did not change significantly, while the association for O3 disappeared. We estimated that 7.72% of the hospital admissions were attributable to CO exposures. No significant or consistent association was observed for exposure to PM2.5, PM10, SO2, or NO2. In conclusion, short-term exposure to ambient CO, even at levels below the WHO air quality guideline, was significantly associated with an increased odds of hospital admissions for sequelae of stroke, which may lead to considerable excess hospital admissions.

Health benefits by attaining the new WHO air quality guideline targets in China: A nationwide analysis

To reduce the high disease burden caused by air pollution, World Health Organization (WHO) issued a new air quality guideline (AQG) on the 22nd September 2021. A timely quantitative assessment of health benefits by meeting these targets is a key measure to advocate and inform national and regional disease control policies. We collected daily major air pollution data in 315 Chinese cities from the 1st January to the 31st December 2019, and the corresponding annual population and mortality rate in the whole population of each city. Then, the mortality benefits were estimated when daily air pollution levels attained WHO's new AQG targets (15 μg/m³ for PM_2.5, 25 μg/m³ for NO₂ and 100 μg/m³ for O₃) in 315 Chinese cities and 31 provinces by using pollutant- and cause-specific concentration-response functions. In total, 134,025 (95%CI: 92,768; 173,029) air pollution-associated non-accidental deaths could be avoided in 315 Chinese cities in 2019 by attaining WHO's new AQG targets, with 43,800 (95%CI: 29,945; 55,616) avoidable deaths from PM_2.5, 58,070 (95%CI: 45,333; 70,714) from NO₂, and 32,155 (95%CI: 17,490; 46,699) from O₃. Cardiovascular diseases and respiratory diseases accounted for 72,698 (95%CI: 46,561; 101,680) and 17,726 (95%CI: 8603; 26,925) avoidable deaths, respectively. Health benefits from reduction in air pollution levels were 99.26 avoided non-accidental deaths per million population at national level, ranging from 12.48 per million in Tibet to 166.26 per million in Hebei. These findings suggest that the compliance with the WHO updated AQG standards would save substantial amount of air pollution-related premature deaths in China. More stringent air pollution control and management measures are urgently warranted to reduce the disease burden from air pollutants in China, particularly for the worsening O₃ pollution.

Short-term exposure to ozone and economic burden of premature mortality in Italy: A nationwide observation study

Italy is among the countries with the highest ozone concentration in Europe. However, the mortality burden of ozone and related economic loss has not been fully characterized. This study aimed to estimate the ozone-mortality association in Italy and evaluate attributable mortality burden and related economic loss in 2015-2019. We collected daily all-cause mortality data stratified by age and sex from 2015 to 2019 in 107 provinces of Italy. A two-stage time-series framework was applied to estimate the association between daily maximum eight-hour average ozone and mortality as well as economic loss. An overall increase in the risk of mortality (RR=1.0043, 95% CI: 1.0029, 1.0057) was associated with every 10 µg/m³ increase in ozone. Generally, a total of 70,060 deaths and $65 billion economic loss were attributed to ozone exposure, corresponding to 3.11% of mortality and about 0.5% of the national GDP during the study period, respectively. The highest ozone-related mortality burden (30,910 deaths) and economic loss ($29.24 billion) were observed in the hot season. This nationwide study suggested considerable mortality burden and economic loss were associated with exposure to ozone. More actions and policies should be proposed to reduce ozone levels and help the public protect their health.

Impact of fueling protocols on emission outcomes for residential wood-fired appliances

Many believe that certification testing of residential wood heat appliances should provide data indicative of installed performance. Operationally, test methods typically only assess steady-state emissions and fail to include other typical conditions for batch appliances such as start-up. From a fueling perspective, protocols should ensure a consistent approach reflecting common use practices. Ensuring representative conditions and accurate quantification of emissions requires assessing the impact of different start-up conditions and whether or not start-up conditions affect appliance operation during start-up and beyond. This study evaluated the impact of modifying fuel piece sizes and configurations using a "smart" wood-fired hydronic heater (WHH) cordwood appliance. The appliance represents technologies using software and oxygen sensors to improve performance. Since the study used a "smart" appliance, the results likely reflect the least amount of variability found in a WHH cordwood appliance. The analysis consisted of a series of tests that involved changing one fuel variable per series, including: (1) kindling fuel arrangement in the firebox; (2) fuel piece size; and (3) the amount of kindling and starter fuel used. A goal of the study was to determine how each variable affects emissions performance during start-up and the following steady state load. Testing used a dual-stage combustion cordwood WHH equipped with external thermal storage. Particulate matter (PM), carbon monoxide (CO), and delivered heating efficiency were measured, and visible emissions from the stack and secondary combustion chamber were observed. Replicate tests were conducted for each protocol series to evaluate WHH performance reproducibility. These tests found that for a low-mass staged combustion WHH with external thermal storage, the use of different fueling protocols can substantially affect PM and CO emissions.Implications: As test methods move to incorporate measurements beyond steady-state emissions, fueling protocols must be assessed to determine (1) if they reflect typical field procedures and (2) the impact of start-up procedures on the complete test run. This paper assessed how changing start-up conditions affected run variability and PM emission impacts.

Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic Density

Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.

Systematic review and meta-analysis of studies between short-term exposure to ambient carbon monoxide and non-accidental, cardiovascular, and respiratory mortality in China

Although a growing number of original epidemiological studies imply a link between ambient pollution exposure and mortality risk, the findings associated with carbon monoxide (CO) exposure are inconsistent. Thus, we conducted a systematic review and meta-analysis of epidemiological studies to evaluate the correlations between ambient CO and non-accidental, cardiovascular, and respiratory mortality in China. Eight databases were searched from inception to 15 May 2021. A random-effect model was used to calculate the pooled relative risks (RRs) and 95% confidence intervals (CIs). Subgroup analyses as well as sensitivity analyses were performed. The I square value (I²) was used to assess heterogeneity among different studies. The assessment of publication bias on included studies was examined by funnel plot and Egger's test. The influence of a potential publication bias on findings was explored by using the trim-and-fill procedure. Ultimately, a total of 19 studies were included in our analysis. The pooled relative risk for each 1 mg/m³ increase of ambient carbon monoxide was 1.0220 (95%CI: 1.0102-1.0339) for non-accidental mortality, 1.0304 (95%CI:1.0154-1.0457) for cardiovascular mortality, and 1.0318 (95%CI:1.0132-1.0506) for respiratory mortality. None of subgroup analyses could explain the source of heterogeneity. Exclusion of any single study did not materially alter the pooled effect estimates. Although it was suggestive of publication bias, findings were generally similar with principal findings when we explored the influence of a potential publication bias using the trim-and-fill method. Our meta-analysis demonstrated that exposure to ambient CO was positive with risk of deaths from all non-accidental causes, total cardiovascular, and respiratory diseases. Based on these findings, tougher intervention policies and initiatives to reduce the health effects of CO exposure should be established.

Ambient air pollution and epileptic seizures: a panel study in Australia

OBJECTIVE

Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures.

METHODS

This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In the NeuroVista dataset, 3273 seizures were recorded using intracranial electroencephalography (iEEG) from 15 participants with refractory focal epilepsy in Australia in 2010-2012. In the seizure diary dataset, 3419 self-reported seizures were collected through a mobile application from 34 participants with epilepsy in Australia in 2018-2021. Daily average concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂ ), ozone (O₃ ), particulate matter ≤10 μm in diameter (PM₁₀ ), and sulfur dioxide (SO₂ ) were retrieved from the Environment Protection Authority (EPA) based on participants' postcodes. A patient-time-stratified case-crossover design with the conditional Poisson regression model was used to determine the associations between air pollutants and epileptic seizures.

RESULTS

A significant association between CO concentrations and epileptic seizure risks was observed, with an increased seizure risk of 4% (relative risk [RR]: 1.04, 95% confidence interval [CI]: 1.01-1.07) for an interquartile range (IQR) increase of CO concentrations (0.13 parts per million), while no significant associations were found for the other four air pollutants in the whole study population. Females had a significantly increased risk of seizures when exposing to elevated CO and NO₂ , with RR of 1.05 (95% CI: 1.01-1.08) and 1.09 (95% CI: 1.01-1.16), respectively. Additionally, a significant association was observed between CO and the risk of subclinical seizures (RR: 1.20, 95% CI: 1.12-1.28).

SIGNIFICANCE

Daily exposure to elevated CO concentrations may be associated with the increased risk of epileptic seizures, especially for subclinical seizures.

A systematic literature review of deep learning neural network for time series air quality forecasting

Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.

The Improvement of Air Quality and Associated Mortality during the COVID-19 Lockdown in One Megacity of China: An Empirical Strategy

Although the lockdown policy implemented during the COVID-19 pandemic indeed improved the air quality and reduced the related health risks, the real effects of the lockdown and its resulting health risks remain unclear considering the effects of unobserved confounders and the longstanding efforts of the government regarding air pollution. We compared air pollution between the lockdown period and the period before the lockdown using a difference-in-differences (DID) model and estimated the mortality burden caused by the number of deaths related to air pollution changes. The NO₂ and CO concentrations during the lockdown period (17 days) declined by 8.94 μg/m³ (relative change: 16.94%; 95% CI: 3.71, 14.16) and 0.20 mg/m³ (relative change: 16.95%; 95% CI: 0.04, 0.35) on an average day, respectively, and O₃ increased by 8.41 μg/m³ (relative change: 32.80%; 95% CI: 4.39, 12.43); no meaningful impacts of the lockdown policy on the PM_2.5, PM₁₀, SO₂, or the AQI values were observed. Based on the three clearly changed air pollutants, the lockdown policy prevented 8.22 (95% CI: 3.97, 12.49) all-cause deaths. Our findings suggest that the overall excess deaths caused by air pollution during the lockdown period declined. It is beneficial for human health when strict control measures, such as upgrading industry structure and promoting green transportation, are taken to reduce emissions, especially in cities with serious air pollution in China, such as Shijiazhuang.