Urban air pollution control policies and strategies: a systematic review

Strategies to reduce air pollution emissions from urban residential buildings

As cities continue to grow, developing mitigation strategies is crucial to minimize the corresponding increase in air pollutants. One source of potentially controllable air pollution is the emissions from residential buildings. We conducted a literature review to systematically examine air pollution emissions from residential buildings in urban areas, identifying pollutants and their sources; investigated mitigation-aimed intervention types by field of application or study, and finally listed and discussed strategies to reduce the concentration of air pollutants in residential buildings. Our compilation shows that among the nature-based solutions, green walls offered the highest relative reduction of air pollution (-15 % NO2 and -23 % PM10). Of the construction-based solutions, already-available photocatalytic paint can achieve reductions of 25 % NO, 23 % NOx and 19 % NO2 as is. Industrial-based solutions promise high levels of reduction, but these must be adapted to residential buildings. The integration of various existing and potentially adapted mitigation solutions may achieve even higher pollution reduction rates in urban areas.

The temporal trend and disparity in short-term health impacts of fine particulate matter in California (2006-2019)

Most epidemiological studies assume that the relationship between short-term air pollution exposure and health outcomes is constant over time, which ignores potential changes in population composition and particulate matter emission sources. Limited studies have assessed changes in the relationship between fine particulate matter (PM2.5) and adverse health outcomes over time, with mixed results. Additionally, there is a need to identify which subgroups are disproportionately impacted over time by PM2.5-related health consequences. Therefore, we aimed to examine whether temporal trends exist in the relationships between daily PM2.5 exposure and circulatory and respiratory acute care utilization in California from 2006 to 2019. We further assessed whether certain subpopulations are more susceptible to PM2.5 exposure by demographic characteristics and extreme wildfire frequency. Daily PM2.5 concentrations estimated from a stacked ensemble model and daily cause-specific acute care utilization and demographic data from the California Department of Health Care Access and Information. We analyzed this relationship using modified two-stage Bayesian hierarchical models, where we first did not consider temporal trends, then stratified by two periods, and finally flexibly considered non-linear changes over time. Increases in circulatory (0.56 %; 95 % credible interval (CI): 0.17 %, 0.96 %) and respiratory acute care utilization risk (2.61 %; 95%CI: 2.29 %, 2.94 %) were found with every 10 μg/m3 increase in PM2.5 on the same day and previous two days. These risks were found to increase over time, where 0.13 % (95%CI: 0.02 %, 0.22 %) and 1.40 % (95%CI: 1.24 %, 1.54 %) increases were identified for circulatory and respiratory acute care utilizations, respectively, from the first (2006-2012) to second period (2013-2019). Differences by age, sex, race/ethnicity, and extreme wildfire frequency were noted. These findings confirm that air pollution guidelines should consider the dynamic nature of epidemiological dose-response and can provide insight for targeted air pollution control and adaptation policies designed to reduce PM2.5 exposure, particularly for the most susceptible subpopulations.

Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods

BACKGROUND

Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, chronic obstructive pulmonary disease (COPD) continues to be a health burden in the United States. In this paper, we focus on COPD in the United States from 2016 to 2019.

OBJECTIVE

We gathered a diverse set of non-personally identifiable information from public data sources to better understand and predict COPD rates at the core-based statistical area (CBSA) level in the United States. Our objective was to compare linear models with machine learning models to obtain the most accurate and interpretable model of COPD.

METHODS

We integrated non-personally identifiable information from multiple Centers for Disease Control and Prevention sources and used them to analyze COPD with different types of methods. We included cigarette smoking, a well-known contributing factor, and race/ethnicity because health disparities among different races and ethnicities in the United States are also well known. The models also included the air quality index, education, employment, and economic variables. We fitted models with both multiple linear regression and machine learning methods.

RESULTS

The most accurate multiple linear regression model has variance explained of 81.1%, mean absolute error of 0.591, and symmetric mean absolute percentage error of 9.666. The most accurate machine learning model has variance explained of 85.7%, mean absolute error of 0.456, and symmetric mean absolute percentage error of 6.956. Overall, cigarette smoking and household income are the strongest predictor variables. Moderately strong predictors include education level and unemployment level, as well as American Indian or Alaska Native, Black, and Hispanic population percentages, all measured at the CBSA level.

CONCLUSIONS

This research highlights the importance of using diverse data sources as well as multiple methods to understand and predict COPD. The most accurate model was a gradient boosted tree, which captured nonlinearities in a model whose accuracy is superior to the best multiple linear regression. Our interpretable models suggest ways that individual predictor variables can be used in tailored interventions aimed at decreasing COPD rates in specific demographic and ethnographic communities. Gaps in understanding the health impacts of poor air quality, particularly in relation to climate change, suggest a need for further research to design interventions and improve public health.

Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 μg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 μg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

Pollution and endometriosis: A deep dive into the environmental impacts on women's health

BACKGROUND

The interaction between pollution and endometriosis is a pressing issue that demands immediate attention. The impact of pollution, particularly air and water pollution, or occupational hazards, on hormonal disruption and the initiation of endometriosis remains a major issue.

OBJECTIVES

This narrative review aims to delve into the intricate connection between pollution and endometriosis, shedding light on how environmental factors contribute to the onset and severity of this disease and, thus, the possible public health policy implications.

DISCUSSION

Endocrine-disrupting chemicals (EDCs) in pollutants dysregulate the hormonal balance, contributing to the progression of this major gynaecological disorder. Air pollution, specifically PM2.5 and PAHs, has been associated with an increased risk of endometriosis by enhancing chronic inflammation, oxidative stress, and hormonal imbalances. Chemical contaminants in water and work exposures, including heavy metals, dioxins, and PCBs, disrupt the hormonal regulation and potentially contribute to endometriosis. Mitigating the environmental impact of pollution is required to safeguard women's reproductive health. This requires a comprehensive approach involving stringent environmental regulations, sustainable practices, responsible waste management, research and innovation, public awareness, and collaboration among stakeholders.

CONCLUSION

Public health policies have a major role in addressing the interaction between pollution and endometriosis in a long-term commitment.

Spatial Distribution of Heavy Metal Contamination in Road Dust Samples from an Urban Environment in Samsun, Türkiye

Road dust is an environmental pollution indicator created by human activities for urban land use. This study aimed to determine the spatial distribution pattern and degree of trace metals in road dust samples collected from 5 different areas in Samsun city center. The trace metals of Mn, Co, Cd, Cr, Cu, Ni, Pb, and Zn are the most examined contaminants in road dust because their hot-spot areas were mainly associated with high traffic density. Factors governing potential contamination index range values of Co, Cr, and Ni were 0.34-0.62, 0.23-0.78, and 0.24-0.48 as the lowest contamination. However, potential contamination index values of Cu, Pb, and Zn in the main road site were 1.80, 2.32, and 2.84 suggesting that relatively high values were uncontaminated to moderately. Pollution assessment methods were applied to toxic metals and revealed that Samsun city had been affected as uncontaminated to moderately contaminated by anthropogenic emission of heavy metals.

What Factors Dominate the Change of PM2.5 in the World from 2000 to 2019? A Study from Multi-Source Data

As the threat to human life and health from fine particulate matter (PM2.5) increases globally, the life and health problems caused by environmental pollution are also of increasing concern. Understanding past trends in PM2.5 and exploring the drivers of PM2.5 are important tools for addressing the life-threatening health problems caused by PM2.5. In this study, we calculated the change in annual average global PM2.5 concentrations from 2000 to 2020 using the Theil-Sen median trend analysis method and reveal spatial and temporal trends in PM2.5 concentrations over twenty-one years. The qualitative and quantitative effects of different drivers on PM2.5 concentrations in 2020 were explored from natural and socioeconomic perspectives using a multi-scale geographically weighted regression model. The results show that there is significant spatial heterogeneity in trends in PM2.5 concentration, with significant decreases in PM2.5 concentrations mainly in developed regions, such as the United States, Canada, Japan and the European Union countries, and conversely, significant increases in PM2.5 in developing regions, such as Africa, the Middle East and India. In addition, in regions with more advanced science and technology and urban management, PM2.5 concentrations are more evenly influenced by various factors, with a more negative influence. In contrast, regions at the rapid development stage usually continue their economic development at the cost of the environment, and under a high intensity of human activity. Increased temperature is known as the most important factor for the increase in PM2.5 concentration, while an increase in NDVI can play an important role in the reduction in PM2.5 concentration. This suggests that countries can achieve good air quality goals by setting a reasonable development path.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km^-1 veh^-1 and 0.057 ± 0.057 g (kg-fuel)^-1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km^-1 veh^-1 and 10.5 ± 2.52 mg km^-1 veh^-1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8-53.6 %, with 0.32 ± 0.45 mg km^-1 veh^-1 and 0.018 ± 0.020 g (kg-fuel)^-1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km^-1 veh^-1 and 3.06 ± 0.91 mg km^-1 veh^-1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Air Pollution and the Heart: Updated Evidence from Meta-analysis Studies

PURPOSE OF REVIEW

Although environmental exposure such as air pollution is detrimental to cardiovascular disease (CVD), the effects of different air pollutants on different CVD endpoints produced variable findings. We provide updated evidence between air pollutants and CVD outcomes including mitigation strategies with meta-analytic evidence.

RECENT FINDINGS

An increased exposure to any class of air pollutants including particulate matter (PM), gas, toxic metals, and disruptive chemicals has been associated with CVD events. Exposure to PM < 2.5 μm has been consistently associated with most heart diseases and stroke as well as CVDs among at-risk individuals. Despite this, there is no clinical approach available for systemic evaluation of air pollution exposure and management. A large number of epidemiological evidence clearly suggests the importance of air pollution prevention and control for reducing the risk of CVDs and mortality. Cost-effective and feasible strategies for air pollution monitoring, screening, and necessary interventions are urgently required among at-risk populations and those living or working, or frequently commuting in polluted areas.

Air Pollution Management: A Multivariate Analysis of Citizens' Perspectives and Their Willingness to Use Greener Forms of Transportation

The present research aims to understand how air pollution can be managed by public authorities, both central and local, starting from citizens' perspectives on the issue. Air quality is a real problem, affecting people at multiple levels. Thus, we introduced the following variables to better understand the problem and to be able to formulate theoretical and practical implications for public management: the involvement of authorities in reducing air pollution; the involvement of citizens in reducing air pollution; financial incentives for citizens and companies for adopting behaviors that reduce air pollution; green investments in the city; the impact of air pollution on the community; and the need for independent bodies to monitor air pollution. The research methodology used is partial least squares structural equation modelling (PLS-SEM) and the required data were gathered from issuing a survey to citizens from the most important cities in Romania where pollution poses important challenges for the community and for the authorities. The results are useful to public managers in local and central institutions for creating better strategies meant to reduce air pollution, increase air quality, and improve the quality of the citizens' lives.