Addressing Air Pollution Research Articles

Total gaseous mercury in Kathmandu, a South Asian metropolis: Temporal variations, sources apportionment and health risk assessment

South Asia is a global hotspot of air pollution gaining attention due to its severe implications, in which atmospheric mercury (Hg) could cause detrimental health effects in metropolitan areas. In this study, first-time year-round (January – December 2019) mean total gaseous mercury (TGM) concentration at Kathmandu, Nepal - a sub-tropical city in South Asia was reported at 9.9 ± 10.0 ng m-3. Seasonal TGM variation at Kathmandu showed highest concentration in winter (16.8 ± 16.9 ng m-3) and lowest in summer (2.9 ± 2.1 ng m-3). Generally higher daytime TGM concentration as opposed to night-time TGM indicated Hg build-up within atmospheric boundary layer due to low wind speed and high humidity. Events with high wind speed (> 30 m s-1) induced regional pollutant transport from nearby brick kilns and cement factories. Principal component analysis associated a major part of TGM with PM2.5 and CO and indicated the remarkable influence of fuel combustion and vehicular emissions. Backward trajectory and potential source contribution factor analysis further indicate the impact of regional Hg emissions and transboundary emissions from India towards Nepal, which expands beyond the Himalayas and the Tibetan Plateau. Prominent low-grade coal burning and kilning activities in winter spiked up TGM concentrations, resulting in the highest health quotient (HQ = 0.24) value, which could significantly impact public health. Our study presented the most comprehensive set of continuous annual atmospheric Hg monitoring data from Nepal in the South Asian region. The results serve as a baseline for regional atmospheric Hg levels and offer a critical reference for assessing and addressing air pollution concerns in Nepal as well as throughout South Asia.

Understanding factors influencing adoption of air pollution protective measures using the knowledge-attitude-behavior model

To address air pollution hazards effectively, it is crucial to understand the factors that influence residents’ adoption of harm reduction measures. Existing studies often focus on passive defense and ignore active strategies such as air purifiers, dehumidifiers, anti-haze window screens, and air-purifying plants. This study adopted the Knowledge–Attitude–Behavior (KAB) model to explore the factors that influence residents to take proactive measures to prevent the impacts of air pollution. This cross-sectional study recruited 371 participants aged 25–64 years living in southern Taiwan. A one-shot online survey was conducted to examine demographic information, air pollution exposure experience, knowledge, risk perceptions, attitudes toward air pollution protective measures, perceived barriers and benefits, payment for protective measures, and actual engagement in protective actions. Structural equation modeling was used to examine the relationships among these factors. This study reveals that the adoption of these protective measures can be understood through three primary pathways. The first pathway linked knowledge to attitude toward behavior. Regarding the various protective measures, this study found that knowledge strongly influenced individuals’ attitudes. This, in turn, influenced their likelihood of engaging in protective measures. The second pathway revealed the effect of risk perception on protective measures, with attitudes mediating this relationship. Perceived risk susceptibility and risk severity were linked to attitudes, which further influenced the adoption of protective measures. The third pathway emphasized the direct effects of perceived barriers on the adoption of protective measures. Individuals who perceived fewer barriers were more likely to adopt these measures. This study fills the gap in the understanding of the factors influencing residents’ adoption of proactive air pollution protection measures. Moreover, this study underscores the importance of increasing public awareness and reducing barriers to promote individual actions for mitigating the negative effects of air pollution.

Air Pollution and Interstitial Lung Disease.

This review article explores the multifaceted relationship between air pollution and interstitial lung diseases (ILDs), particularly focusing on idiopathic pulmonary fibrosis (IPF), the most severe form of fibrotic ILD. Air pollutants are mainly composed of particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). They are recognized as risk factors for several respiratory diseases. However, their specific effects on ILDs and related mechanisms have not been thoroughly studied yet. Emerging evidence suggests that air pollutants may contribute to the development and acute exacerbation of ILDs. Longitudinal studies have indicated that air pollution can adversely affect the prognosis of disease by decreasing lung function and increasing mortality. Lots of in vitro, in vivo, and epidemiologic studies have proposed possible mechanisms linking ILDs to air pollution, including inflammation and oxidative stress induced by exposure to air pollutants, which may induce mitochondrial dysfunction, promote cellular senescence, and disrupt normal epithelial repair processes. Despite these findings, effective interventions to mitigate effects of air pollution on ILD are not well established yet. This review emphasizes the urgent need to address air pollution as a key environmental risk factor for ILDs and calls for further studies to clarify its effects and develop preventive and therapeutic strategies.

Legal perspectives on climate change and inequalities

Abstract In many areas of public health policy, litigation has delivered significant, long-lasting impacts. Examples include tobacco control, increasing access to treatments for HIV, and addressing air pollution and climate change. Litigation is an established part of public health’s advocacy toolbox in national courts, and increasingly in international courts and tribunals. In responding to climate change, in addition to potential policy and law reform, climate change litigation creates opportunities for public scrutiny and debate by raising awareness of inaction or harm caused by governments or private sector polluters, and sparking wider community mobilisation for climate action. Where climate change litigation is based on human rights claims, credible science-based evidence of the health and other human rights impacts of climate change is essential. Public health professionals are increasingly involved in assembling and presenting such evidence, often focusing on vulnerable populations. However, the multiple intersecting identities of vulnerable populations have not been sufficiently explored. For example, how does intersectional disadvantage influence the ways in which evidence of the impact of climate change is assembled and assessed? How are these issues addressed in the courts? Examples include older women (KlimaSeniorinnen v. Switzerland), young people, from both the global South and global North, in urban or remote areas, or with different pre-existing health conditions (Sacchi et al. v. Argentina et al), and Indigenous people, including women and children (Daniel Billy and others v. Australia; Teitiota and others v. New Zealand). Multiple identities can compound vulnerability. For example, young people may be more vulnerable both due to their physical immaturity and due to eco-anxiety and other mental health impacts. This presentation will explore these issues, and the critical role of public health professionals in legal action to address climate change.

Air quality disparities and respiratory health risks in critically polluted and relatively non-polluted areas: a prospective child health study.

Air pollution, a significant global health concern, notably impacts human well-being. Children, owing to their distinctive physiology and behavior, are particularly susceptible to its adverse effects. This prospective study examines air quality variations and respiratory risks in children residing in critically polluted areas (CPA) compared to relatively non-polluted areas (NPA), utilizing a prospective design to understand the impacts of air pollution on children's respiratory health, including measures like relative risk (RR) and attributable risk (AR). This prospective study tracked 739 students of 5th- 7th grade residing in CPA and NPA for one year, and measured the ambient and indoor air quality levels in both these areas. Throughout the study, based on the observed respiratory symptoms new episodes of upper and lower respiratory tract illnesses were recorded for each child on a weekly basis. Incidence rate, RR and AR for both the illnesses were compared. Statistical analysis was performed with SPSS 26.0. The study observes higher concentration for particulate matter and gaseous pollutants at CPA in comparison to NPA. Children living in CPA exhibited a notably greater weekly occurrence of both upper and lower respiratory tract illnesses compared to those in NPA, with RR of 1.26 (95% CI: 1.16-1.37) and 1.74 (95% CI: 1.34-2.27), respectively. The AR associated with air pollution for upper and lower respiratory tract illnesses among CPA students was found to be 20.7% and 42.7%, respectively. This research underscores the pressing need to address air pollution in critically polluted areas and its profound effects on children's respiratory health. Public health interventions, such as reducing emissions from industries and creating green spaces, should be prioritized. Furthermore, early respiratory health screening in schools within polluted areas could aid in the timely diagnosis and management of respiratory issues in children.

Pollutant Dispersion of Aircraft Exhaust Gas during the Landing and Takeoff Cycle with Improved Gaussian Diffusion Model

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To assess the pollutant emissions and dispersion of aircraft during the landing and takeoff (LTO) cycle, and address air pollution surrounding the airport resulting from flight operations, this study evaluated emissions throughout the LTO phase based on Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources was employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability. Specifically, a change in wind direction from 0° to 45° caused a shift of approximately 1000 m in the contaminant’s center. An increase in wind speed from 3 m/s to 5 m/s led to a decrease in concentration by about 15%. Furthermore, a transition in atmospheric stability from category ‘a’ (very unstable) to ‘f’ (very stable) resulted in a two-order-of-magnitude increase in contaminant concentrations.

Time Series Forecasting of Air Quality Index in Lahore : A Machine Learning Perspective with Facebook Prophet Model

This research investigates the rising problem of air pollution, which is a serious issue affecting people's health and the environment on a global scale. Focusing on Lahore, Pakistan, a city facing severe pollution, the research examines the Air Quality Index (AQI) from May 2019 to December 2023. Using the Facebook Prophet model for time-series forecasting, the analysis reveals trends at monthly, weekly, and hourly intervals. Peak AQI concentrations are identified in winter months, notably December and January, aligning with hazardous smog levels. Weekly trends show minimum AQI on weekends, particularly Saturday and Sunday. The hourly trend points to peak AQI concentrations between 7:00 and 09:00 AM, correlating with morning traffic and emphasizing the role of vehicular emissions in air quality degradation. These forecasts are crucial for anticipating and mitigating the adverse impact of air pollution on human health. This research underscores the critical need for comprehensive strategies to address air pollution, acknowledging the Lahore High Court and Government's initiatives, such as artificial rain experimentation, closure of commercial markets by 10 PM, ban on cutting trees and smoke-emitting vehicles. In summary, this research significantly contributes to understanding and tackling the increasingly urgent challenges associated with air quality.

The impact of latent policy synergy between cities on air pollution: An empirical analysis from China's 12,764 air policies

Policy synergy plays a crucial role in managing regional air pollution. However, the current process of formulating collaborative policies often focuses too heavily on achieving optimal emission reduction outcomes, while neglecting the execution costs at the local level. This not only leads to a lack of coordination between existing governance measures and new policies but may also result in inefficient policy implementation and the wastage of administrative resources. To address this issue, we have conducted a thorough analysis of the latent interconnections in local governance and explored the possibility of formulating collaborative policies based on latent policy synergies. Utilizing a dataset of 12,764 policy documents from China spanning from 2000 to 2023, we employed unsupervised learning techniques and panel regression models to investigate the impacts of latent policy synergies among local governments. Our findings reveal that local governments initially relied on rigid command-and-control strategies to address air pollution, which were later enhanced with economic incentives like emissions trading and subsidies, thus broadening the scope of policy tools and improving compliance flexibility. Subsequently, as comprehensive monitoring networks were established, enabling real-time data acquisition and more accurate pollution assessments, the focus of policy efforts shifted towards a more integrated approach to regional air pollution management. This similar evolution facilitated latent policy synergy among local governments, leading to significant reductions in air pollution emissions. Notably, regions such as the Central Plains and coastal cities, characterized by their dense industrial activity and high energy consumption, have shown remarkable potential for collaborative efforts, achieving greater pollution control efficiencies. This study provides a valuable reference for the coordinated governance of air pollution policies.

Emerging Epidemic: Non-Smoker Lung Cancer in India\'s Urban Centres

Abstract: Recent research has revealed a concerning trend in the incidence of lung cancer among non-smokers in India, particularly within urban centers. Contrary to the traditional association of lung cancer with smoking, a significant proportion of cases in India are now emerging in individuals with no history of tobacco use. This phenomenon is primarily attributed to severe air pollution, which has become a critical public health issue in many Indian cities. Rapid industrialization and urbanization have led to increased exposure to airborne pollutants, including fine particulate matter (PM2.5), nitrogen dioxide (NO2), and other carcinogens, which are now recognized as significant risk factors for lung cancer. Genetic predispositions further exacerbate this risk, with studies indicating that certain populations are more susceptible to developing lung cancer due to genetic mutations influenced by environmental exposures. These mutations and biomarkers, such as EGFR and ALK gene rearrangements, have been increasingly identified in Indian non-smoker lung cancer patients, providing critical insights into the disease's etiology and potential targeted therapies. India currently ranks fourth globally in lung cancer prevalence, with urban areas experiencing the highest growth rates. Projections suggest that the incidence of lung cancer will continue to rise, potentially reaching alarming levels by 2025. This emerging epidemic highlights the urgent need for comprehensive public health strategies to address air pollution and its associated health risks. Efforts must be directed towards reducing pollution levels through stringent regulatory measures, promoting cleaner technologies, and enhancing public awareness about the health impacts of air pollution. Additionally, implementing widespread screening programs and advancing research on genetic and environmental interactions can aid in early detection and personalized treatment approaches for affected individuals. In conclusion, the rising incidence of lung cancer among non-smokers in urban India signifies a shifting landscape in the disease's epidemiology. Addressing this public health challenge requires an integrated approach that combines environmental, genetic, and healthcare interventions to mitigate the growing burden of lung cancer in non-smoking populations.

PUBLIC HEALTH POLICY RESPONSES TO AIR POLLUTION IN INDIA: CHALLENGES AND STRATEGIES FOR SUSTAINABLE DEVELOPMENT

This study investigates the public health policy responses to air pollution in India, highlighting the challenges and strategies needed for sustainable development. Despite significant efforts to address air pollution through policies such as the National Clean Air Programme (NCAP), the effectiveness of these measures remains limited. The research involved a systematic review of existing literature, policy documents, and case studies from various Indian cities. Data were collected from government reports, academic journals, and interviews with public health and environmental experts. Key findings indicate persistent issues with policy enforcement, inadequate public awareness, and insufficient coordination among agencies. These challenges impede the achievement of meaningful reductions in air pollution levels and improved public health outcomes. The study emphasizes the need for more comprehensive and robust policy frameworks to address these issues effectively. Recommendations include enhancing policy implementation, increasing public engagement, and leveraging advanced technologies for air quality monitoring and management. Addressing these gaps is crucial for advancing sustainable development and mitigating the adverse health impacts of air pollution. The research provides actionable insights for policymakers and stakeholders aiming to improve air quality and public health in India.

Changes on Stroke Burden Attributable to Ambient Fine Particulate Matter in China

Objective In recent decades, China has implemented a series of policies to address air pollution. We aimed to assess the health effects of these policies on stroke burden attributable to ambient fine particulate matter (PM2.5).MethodsJoinpoint regression was applied to explore the temporal tendency of stroke burden based on data from the Global Burden of Disease 2019 study.Results The age-standardized rates of disability-adjusted life year (DALY) for stroke attributable to ambient PM2.5 in China, increased dramatically during 1990–2012, subsequently decreased at an annual percentage change (APC) of –1.98 [95% confidence interval (CI): –2.26, –1.71] during 2012–2019. For ischemic stroke (IS), the age-standardized DALY rates doubled from 1990 to 2014, and decreased at an APC of –0.83 (95% CI: –1.33, –0.33) during 2014–2019. Intracerebral hemorrhage (ICH) showed a substantial increase in age-standardized DALY rates from 1990 to 2003, followed by declining trends, with APCs of –1.46 (95% CI: –2.74, –0.16) during 2003–2007 and –3.33 (95% CI: –3.61, –3.06) during 2011–2019, respectively. Conversely, the age-standardized DALY rates for subarachnoid hemorrhage (SAH) generally declined during 1990–2019.Conclusion Our results clarified the dynamic changes of the ambient PM2.5–attributable stroke burden in China during 1990–2019, highlighting the health effects of air quality improvement policies.

Spatiotemporal of Particulate Matter (PM2.5) and Ozone (O3) in Eastern Northeast Brazil

ABSTRACT This research examines particulate matter with a diameter of 2.5 micrometers (PM2.5) and ozone (O3) variation across the climatic mesoregions of Alagoas. Mean PM2.5 and O3 concentrations across three mesoregions were as follows: East (5.91; 44.22 μg.m−3), Hinterland (6.90; 44.18 μg.m−3), and Arid (7.03; 44.23 μg.m−3). Spatial and temporal variations were observed, with PM2.5 concentrations highest in the northwest (NW) and O3 concentrations in the east (E), influenced by local sources, weather, and transportation. Differences between rainy and dry years were noted, attributed to biomass burning and dust particle transport. This study establishes a baseline for understanding air quality, lacking monitoring stations, aiding policymaking. It provides insights into PM2.5 and O3 dynamics, with PM2.5 concentrations highest in the NW and lower O3 concentrations in the E. Temporal variations emphasize the need for dynamic monitoring. Positive correlations between PM2.5 and O3 highlight complex relationships. Practical implications include proactive policymaking and the necessity for monitoring stations. Rigorous statistical methodologies inform model selection, enhancing environmental data understanding. Despite changes in land use, studies on extreme probability distributions are limited, emphasizing the need for robust methodologies for environmental data analysis to address air pollution challenges effectively in Alagoas.

Spatial Dynamic Interaction Effects and Formation Mechanisms of Air Pollution in the Central Plains Urban Agglomeration in China

Accurately identifying the dynamic interaction effects and network structure characteristics of air pollution is essential for effective collaborative governance. This study investigates the spatial dynamic interactions of air pollution among 30 cities in the Central Plains Urban Agglomeration using convergent cross mapping. Social network analysis is applied to assess the overall and node characteristics of the spatial interaction network, while key driving factors are analyzed using an exponential random graph model. The findings reveal that air pollution levels in the Central Plains Urban Agglomeration initially increase before they decrease, with heavily polluted cities transitioning from centralized to sporadic distribution. Among the interactions, Heze’s air pollution impact on Kaifeng was the strongest, while Xinxiang’s impact on Changzhi was the weakest. The emission and receiving effects peaked during 2010–2012. The air pollution interactions among cities exhibit significant network characteristics, with block model results indicating that emitting and receiving relationships are primarily concentrated in the bidirectional spillover plate. Natural factors such as temperature and precipitation significantly influence the spatial interaction network. Economic and social factors like economic level and industrial sector proportion also have a significant impact. However, population density does not influence the spatial interaction network. This study contributes to understanding the spatial network of air pollution, thereby enhancing strategies for optimizing regional collaborative governance efforts to address air pollution.

Integrated Benefits of Synergistically Reducing Air Pollutants and Carbon Dioxide in China.

China's advancements in addressing air pollution and reducing CO2 emissions offer valuable lessons for collaborative strategies to achieve diverse environmental objectives. Previous studies have assessed the mutual benefits of climate policies and air pollution control measures on one another, lacking an integrated assessment of the benefits of synergistic control attributed to refined measures. Here, we comprehensively used coupled emission inventory and response models to evaluate the integrated benefits and synergy degrees of various measures in reducing air pollutants and CO2 in China during 2013-2021. Results indicated that the implemented measures yielded integrated benefits value at 6.7 (2.4-12.6) trillion Chinese Yuan. The top five contributors, accounting for 55%, included promoting non-thermal power, implementing end-of-pipe control technologies in power plants and iron and steel industry, replacing residential scattered coal, and saving building energy. Measures demonstrating high synergies and integrated benefits per unit of reduction (e.g., green traffic promotion) yielded low benefits mainly due to their low application, which are expected to gain greater implementation and prioritization in the future. Our findings provide insights into the effectiveness and limitations of strategies aimed at joint control. By ranking these measures based on their benefits and synergy, we offer valuable guidance for policy development in China and other nations with similar needs.

The driving mechanisms of industrial air pollution spatial correlation networks: A case study of 168 Chinese cities

To enhance regional collaborative management and effectively address air pollution, this paper constructs the industrial air pollution network of 168 cities in China between 2010 and 2021. Social network analysis (SNA) and temporal exponential random graph model (TERGM) are used to investigate the structural characteristics and driving mechanisms of industrial air pollution spatial correlation networks. The results indicate the following: (1) Industrial air pollution levels in key Chinese cities showed a slight fluctuation between 2010 and 2015, but decreased after 2015. (2) The industrial air pollution linkage network exhibits small-world characteristics and path dependence, with different cities playing distinct roles in the network. (3) The evolution of the network is influenced by both exogenous and endogenous mechanisms. Local network structures such as reciprocity, connectivity, and transitivity make the network exhibit path-dependent characteristics. Cities’ attributes such as economic development level, green innovation capacity, and population density significantly influenced their status in the network and linkages between cities. Pollution linkages are more likely to occur in cities with similar levels of economic development, green technological innovation capacity, and institutional environments. Therefore, effective regional division for scientific collaborative governance should be considered from multiple perspectives. Based on these results, the paper provides several policy recommendations.

ACPM Position Statement: Air Pollution and Environmental Justice

The American Lung Association's “State of the Air” 2023 report reveals almost 36% of Americans live with unhealthy levels of air pollution. Studies link air pollution with acute respiratory symptoms and exacerbation of respiratory and cardiovascular diseases. Differential air pollution exposures between white and non-white communities are significant components of environmental injustices. Even during the COVID-19 lockdown, when the United States experienced significant decreases in polluting activities, these differences persisted. The American College of Preventive Medicine's Science and Translation Committee conducted a nonsystematic literature review to explore initiatives addressing air pollution as a key component of environmental justice, the state of the science regarding health impacts, and evidence supporting mitigations to reduce those impacts. We recommend advocacy for cleaner energy sources and increasing green space; and increasing research, surveillance, and education and training on linkages between air pollutants and health. We recommend preventive medicine physicians raise awareness about increased risks of cardiovascular disease, cancer, asthma, and reduced lung function with air pollution exposure. Preventive medicine physicians may also educate patients and other practitioners about exposures, and how “conventional” disease prevention strategies may have unintended consequences; and influence healthcare leaders to improve efficiency and reduce emissions. We also recommend physicians utilize social determinants of health (SDOH) Z-Codes to capture environmental factors. Private payers should incorporate pollution exposure data into SDOH risk-adjustments for Medicare Advantage programs. Medicaid agencies should develop provider recommendations for pediatric populations, and states should finance in-home interventions for asthma.

How Does Air Pollution Impact Residence Intention of Rural Migrants? Empirical Evidence from the CMDS

Based on data from the China Migrant Dynamic Survey project and urban statistics, this article examines the impact of air pollution on the residence intentions of rural migrants. The research findings indicate that: (1) Air pollution reduces the residence intentions of rural migrants. On average, for every one-unit increase in AQI, the residence intention of rural migrants will reduce by 1.5l%. (2) Mechanism analysis shows that social networks and social integration have a negative moderating effect on the relationship between air pollution and the residence intention of rural migrants. (3) Heterogeneity analysis shows that in cities north of the Qinling Mountains-Huaihe River, cities with low precipitation, and cities with weak environmental regulations, the negative impact of air pollution on residence intention of rural migrants is more significant. Compared with high human capital levels, inter-provincial flow, and the new generation of rural migrants, the residence intention of low human capital levels, intra-provincial flow, and the old generation of rural migrants makes them more vulnerable to the negative impact of air pollution. This article reveals the inherent relationship between air pollution and the residence intention of rural migrants, which has certain practical enlightenment for cities to accelerate the process of citizenization of rural migrants through air pollution control and also provides important empirical evidence for cities to sustainably address air pollution.

Estimating Chemical Concentrations of Dust PM2.5 in Iraq: A Climatic Perspective Using Polynomial Model and Remote Sensing Technology

Air pollution and climate change are interrelated issues, with air pollution levels in Iraq currently exceeding World Health Organization standards. This study aimed to evaluate air quality in Iraq by utilizing climatic data, such as temperature, humidity, and gaseous pollutants for assessing the health effects based on processed and estimated data. The research was conducted between August and November 2020, using remotely sensed images and geographical information techniques. Two methods; Geographic Information Systems GIS-based multiple regression and a polynomial model, were employed to estimate PM2.5 levels in the study area. The results showed a significant influence of climatic variables on air pollution in Iraq, with varying effects on PM2.5 estimation. The health impact ranged from good to unhealthy, with most provinces experiencing poor air quality. Southern parts of Iraq exhibited PM2.5 levels surpassing the healthy threshold. The predictive linear and polynomial model's accuracy was assessed through regression, yielding high correlation coefficients (R2 ) of 0.89, 0.95, 0.98, and 0.96 for August to November, respectively. While model validation accuracy ranged between 85–94 %. The study emphasizes the vital role of climate data in understanding the dispersion of air pollutants and their significant impacts on the environment. Addressing air pollution and climate change, as per the SGS-13 "Climate Action", are interconnected and require comprehensive strategies for mitigation.

Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.