Air Pollution Research Articles

Enhancing the Reliability of NO2 Monitoring Using Low-Cost Sensors by Compensating for Temperature and Humidity Effects

The study investigates methods to enhance the reliability of NO2 monitoring using low-cost electrochemical sensors to measure gaseous pollutants in air by addressing the impacts of temperature and relative humidity. The temperature within a plastic container was controlled using an internal mica heater, an external hot air blower, or cooling packs, while relative humidity was adjusted using glycerine solutions. Findings indicated that the auxiliary electrode signal is susceptible to temperature and moderately affected by relative humidity. In contrast, the working electrode signal is less affected by temperature and relative humidity; however, adjustments are still required to determine gas concentrations accurately. Tests involving on/off cycles showed that the auxiliary electrode signal experiences exponential decay before stabilizing, requiring the exclusion of initial readings during monitoring activities. Additionally, calibration experiments in zero air allowed the determination of the compensation factor nT across different temperatures and humidity levels. These results highlight the importance of compensating for temperature and humidity effects to improve the accuracy and reliability of NO2 measurements using low-cost electrochemical sensors. This refinement makes the calibration applicable across a broader range of environmental conditions. However, the experiments also show a lack of repeatability in the zero air calibration.

Advanced Trans-BiGRU-QA Fusion Model for Atmospheric Mercury Prediction

With the deepening of the Industrial Revolution and the rapid development of the chemical industry, the large-scale emissions of corrosive dust and gases from numerous factories have become a significant source of air pollution. Mercury in the atmosphere, identified by the United Nations Environment Programme (UNEP) as one of the globally concerning air pollutants, has been proven to pose a threat to the human environment with potential carcinogenic risks. Therefore, accurately predicting atmospheric mercury concentration is of critical importance. This study proposes a novel advanced model—the Trans-BiGRU-QA hybrid—designed to predict the atmospheric mercury concentration accurately. Methodology includes feature engineering techniques to extract relevant features and applies a sliding window technique for time series data preprocessing. Furthermore, the proposed Trans-BiGRU-QA model is compared to other deep learning models, such as GRU, LSTM, RNN, Transformer, BiGRU, and Trans-BiGRU. This study utilizes air quality data from Vietnam to train and test the models, evaluating their performance in predicting atmospheric mercury concentration. The results show that the Trans-BiGRU-QA model performed exceptionally well in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2), demonstrating high accuracy and robustness. Compared to other deep learning models, the Trans-BiGRU-QA model exhibited significant advantages, indicating its broad potential for application in environmental pollution prediction.

Spatio-temporal analysis of bicyclists' PM2.5 exposure levels in a medium sized urban agglomeration.

Many cities have promoted decarbonized transportation modes to mitigate climate change, reduce air pollution and promote healthy behaviors. However, cyclists may be particularly exposed to higher concentrations of on-road air pollutants than other commuters due to their proximity to traffic, higher breathing rates, and prolonged commutes. In addition, there is scarce information analyzing the geographic exposure levels of cyclists in medium-sized urban agglomerations of Latin American cities. We aimed to assess cyclists' exposure to PM2.5 at the spatio-temporal level using low-cost sensors in the Mendoza Metropolitan Area, Argentina. We calculated PM2.5 inhalation doses (IDs) for different routes with distinctive characteristics, considering different age ranges and gender of cyclists. The dose was represented as temporally and spatially disaggregated exposure maps, one of the first to represent it in Latin America using this method. All link types analyzed exhibited significant differences in PM2.5 concentrations, although the most frequent concentrations were less than 5µgm-3, with secondary peaks of 6.5 and 9µgm-3. As expected, ID increases with age and is greater in males than in females. Our findings further reinforce the fact that route choice (busy vs. quiet) and time of day (peak vs. off-peak) appreciably affect the pollutant exposure of cyclists. All these results could be helpful in the selection of alternative cycling routes with lower PM2.5 at different hours of day but also enable us to investigate further implications of exposure to PM2.5 for the health of urban bicycle commuters.

The impact of digital technology and technological innovation on environmental attitudes——insights from a cross-country survey

ABSTRACT The evolving landscape of digital technologies and technological innovations is reshaping our relationship with the environment. While empirical studies at the micro level have extensively examined the separate impacts of these advancements on environmental attitudes, a significant gap persists in understanding their collective influence at the macro level. To address this gap, our study investigates the impact of digital technology and technological innovation on environmental attitudes using data from 44,622 surveys across 28 countries, sourced from the International Social Survey Program. Our analysis reveals that country-level digital technology and technological innovation significantly influence individuals’ environmental attitudes. At the individual level, factors such as air pollution, the quality of the individual’s living environment, and education level play crucial roles in shaping these attitudes. Additionally, public trust in the media plays a significant moderating role in the relationship between digital technology and environmental attitudes. Our research contributes to the literature by simultaneously examining these factors at both individual and national levels. To gain citizens’ attitudes toward environmental protection, there is a need to strengthen the integration of technological innovation and environmental protection and to improve the authenticity of mass media materials while focusing on environmental governance.

The Impact of Carbon Emissions Trading Policy on Regional Economy and Pollution Reductions in Chinese Provinces

Carbon emissions trading policy is an important tool to achieve carbon peaking and carbon neutrality goals. In order to explore the effectiveness of carbon emissions trading policy, this paper adopts the difference-in-differences model to analyze the effects of China’s pilot policy on regional economic development and pollution reductions. The results show that carbon trading policy can significantly promote economic development and reduce total carbon emissions, industrial SO2 emissions and solid wastes production of pilot regions. Further research finds that there is significant regional difference in policy effectiveness, with the policy more effective in western areas. In addition to direct effects, carbon trading policy could exert an indirect effect on carbon emissions, air quality, industrial solid wastes and regional economic development by optimizing energy consumption structures and industrial structures and increasing technological investment. This result verifies the “Porter hypothesis”. China should improve the construction of national carbon trading markets, covering more industries, considering the regional differences and negative spillover effect.

Spatial Analysis of Lung Cancer Patients and Associated Influencing Factors from the Perspective of Urban Sustainable Development: A Case Study of Jiangsu Province, China

With global environmental changes, lung cancer has become one of the most common types of cancer worldwide, posing a significant public health challenge. Jiangsu Province, located in the eastern part of China, is an economically and socially developed region. According to the latest cancer registration data in Jiangsu Province, lung cancer ranks first in both incidence and mortality of cancer in the province. Thus, studying the spatiotemporal distribution of lung cancer cases and analyzing the influence of various factors on this distribution are crucial for the effective prevention and control of the disease in Jiangsu Province. This study takes the statistical data of lung cancer patients in Jiangsu Province in 2020 as the research object, uses Geographic Information System (GIS) visualization and spatial analysis to study the spatial distribution characteristics of lung cancer patients in Jiangsu Province, and employs the geographical detector to numerically express the impact of various environmental factors on the distribution of lung cancer patients in Jiangsu Province. The results reveal a notable spatial clustering of lung cancer cases, with high-incidence areas concentrated in Suzhou, Nanjing, and Xuzhou cities. Among the seven environmental factors examined, PM2.5, SO2, and PM10 concentration exert the most significant influence. This study employs multifactorial spatial analysis to elucidate the intricate relationships between people’s health and air quality, medical resource distribution, and lung cancer incidence in the process of pursuing sustainable development in cities and provides an important reference for the improvement in lung cancer prevention and control strategies.

Brown Carbon in East Asia: Seasonality, Sources, and Influences on Regional Climate and Air Quality

Brown Carbon in East Asia: Seasonality, Sources, and Influences on Regional Climate and Air Quality

Performance evaluation of UKESM1 for surface ozone across the pan-tropics

Abstract. Surface ozone monitoring sites in the tropics are limited, despite the risk that surface ozone poses to human health, tropical forest and crop productivity. Atmospheric chemistry models allow us to assess ozone exposure in unmonitored locations and evaluate the potential influence of changing policies and climate on air quality, human health and ecosystem integrity. Here, we utilise in situ ozone measurements from ground-based stations in the pan-tropics to evaluate ozone from the UK Earth system model, UKESM1, with a focus on remote sites. The study includes ozone data from areas with limited previous data, notably tropical South America, central Africa and tropical northern Australia. Evaluating UKESM1 against observations beginning in 1987 onwards, we show that UKESM1 is able to capture changes in surface ozone concentration at different temporal resolutions, albeit with a systematic high bias of 18.1 nmol mol−1 on average. We use the diurnal ozone range (DOR) as a metric for evaluation and find that UKESM1 captures the observed DOR (mean bias of 2.7 nmol mol−1 and RMSE of 7.1 nmol mol−1) and the trend in DOR with location and season. Results from this study reveal that hourly ozone concentrations from UKESM1 require bias correction before use for impact assessments based on human and ecosystem health. Indeed, hourly surface ozone data have been crucial to this study, and we encourage other modelling groups to include hourly surface ozone output as a default.

Oxide-Perovskites for Automotive Catalysts Biotransform and Induce Multicomponent Clearance and Hazard.

Oxide-perovskites designed for automotive catalysts contain multiple metal elements whose presence is crucial to achieving the targeted performance. They are highly stable in exhaust operating conditions; however, little is known about their stability under physiological conditions. As some of the metallic components are hazardous to humans and the environment, perovskite benefits in cleaner air must be balanced with risks in a Safe and Sustainable Design (SSbD) approach. New approach methodologies (NAMs), including in chemico and in silico methods, were used for testing hazards and benefits, including catalytic activity and tolerance for temporary excess of oxygen under dynamic driving conditions. The composition and surface properties of six different lanthanum-based oxide-perovskites compromised their stability under lung physiological conditions, influencing the oxidative damage of the particles and the bioacessibility of leaching metals. We found consistent biotransformation of the oxide-perovskite materials at pH 4.5. The leached lanthanum ions, but not other metals, respeciated into lanthanum phosphate nanoparticles, which increased the overall oxidative damage in additive synergy. The NAM results in the presented SSbD approach were challenged by in vivo studies in rats and mice, which confirmed multicomponent clearance from lungs into urine and supported the comparative ranking of effects against well-characterized spinel materials. Among the perovskites, the version with reduced nickel content and doped with palladium offered the best SSbD balance, despite not improving the conventional benchmark catalytic performance and related sustainability benefits. Redesign by industry may be necessary to better fulfill all SSbD dimensions.

Two types of microorganisms isolated from petroleum hydrocarbon pollutants: Degradation characteristics and metabolic pathways analysis of petroleum hydrocarbons.

The petroleum hydrocarbons in seawater have been worldwide concern contaminants. Biological method, with the advantages of low cost, minimal environmental impact, and no secondary pollution, is a promising method for petroleum hydrocarbon treatment. In this study, two strains, identified as Stenotrophomonas acidaminiphila and Ochrobactrum, were demonstrated to possess the ability to degrade petroleum hydrocarbons. The mixed culture composed of Stenotrophomonas acidaminiphila and Ochrobactrum at a 2:1 ratio was able to achieve 79.41% degradation of the total petroleum hydrocarbons after 5 days. Besides, the average removal efficiencies of C10-C30 components in petroleum hydrocarbons by Stenotrophomonas acidaminiphila, Ochrobactrum, and mixed culture were 62.98%, 59.14% and 73.30%, respectively. The possible degradation pathways of petroleum hydrocarbons had been speculated through gas chromatography-mass spectrometry (GC-MS) and differential gene expression metabolomics analyses. The toxicity of products from the biodegradation of petroleum hydrocarbons was greatly reduced.

ANALISIS KONTRIBUSI KEPADATAN KENDARAAN BERMOTOR TERHADAP KONSENTRASI KARBON MONOKSIDA (CO) DAN TINGKAT KEBISINGAN DI JALAN SLAMET RIYADI SURAKARTA

<div class="WordSection1"><p><em>In the current era, the transportation is growing rapidly. It has an impact such as air and noise pollution. Carbon monoxide is one of the dangerous pollutants. While the noise comes from the sound of the vehicle engine, horn, and exhaust. Slamet Riyadi street Surakarta is a protocol road that has a high level of vehicle density. So it is necessary to conduct research with the aim of knowing the effect of motor vehicle density on carbon monoxide concentrations and noise levels. The results showed that the concentration of Carbon monoxide on Slamet Riyadi street Surakarta in the morning and evening exceeded the quality standard of 16038.26 µg/Nm<sup>3</sup></em><em>and 16141.58 µg/Nm<sup>3</sup></em><em>. During the day it is still below the quality standard, which is 9425.86 µg/Nm<sup>3</sup></em><em>. Meanwhile, the daytime noise level on Slamet Riyadi street Surakarta which is calculated by the Leq formula also exceeds the quality standard of 82.33 dBA.</em></p></div>

Indoor Air Quality in an Orthopedic Hospital from Romania

Inside hospitals, there is a trend of increasing levels of air pollutants. However, only the indoor air quality in operating theaters is carefully monitored. Therefore, we set the goal of this study to evaluate the indoor air quality in areas of an orthopedics department and to compare the indoor air quality indices that characterize these areas. We used a monitoring system based on the Internet of Things with uRADMonitor model A3 sensors, with which we prospectively measured indoor air quality in the facilities of the orthopedic emergency hospital of Targu Mures in Romania, between 1 February 2023, and 31 January 2024. The primary target pollutants investigated in the emergency room, outpatient room and ward were carbon dioxide (CO2), nitrogen dioxide (NO2), volatile organic compounds (VOCs) and particles with a diameter smaller than 2.5 μm (PM2.5). We compared the effectiveness of the intervention for emergency rooms where air purifiers were working or not. The concentrations of CO2, VOCs and PM2.5 were significantly higher in the emergency room than in the outpatient room or ward. The indoor air quality was worst in winter, when the CO2, NO2 and VOC concentrations were at their highest. Air purifiers can help reduce the concentration of PM2.5 in emergency rooms. Medical staff and patients in orthopedic hospitals, especially in emergency rooms, are frequently exposed to polluted ambient air, which can affect their health. Orthopedic medical practice guidelines should address issues relating to the protection of personnel through the application of measures to improve indoor air quality.

Association between fine particulate matter and eczema: A cross-sectional study of the All of Us Research Program and the Center for Air, Climate, and Energy Solutions.

The prevalence of eczema has increased with industrialization. Industrial practices generate ambient air pollution, including fine particulate matter of diameter ≤ 2.5μm (PM2.5). Studies investigating the relationship between PM2.5 and eczema in the US are scarce. The objective of this study was to determine the risk of eczema with PM2.5 exposure in a diverse national cohort of American adults. In this cross-sectional study, eczema cases in the All of Us Research Program were linked via three-digit zip code to average annual PM2.5 concentrations from the Center for Air, Climate, and Energy Solutions. Eczema cases and controls were compared using Pearson's χ2 test for categorical variables and one-way analysis of variance for continuous variables. The relationship between PM2.5 and eczema was assessed via logistic regression adjusting for demographic factors, smoking, and atopic comorbidities. Individuals with eczema (n = 12,695) lived in areas with significantly higher PM2.5 concentrations than did individuals without eczema (n = 274,127) (0.83 x 10 μg/m3 v. 0.81 x 10 μg/m3, P < .001). PM2.5 concentration was significantly associated with eczema in univariable analysis (odds ratio 1.97, 95% confidence interval 1.77-2.19, P < .001), and in multivariable analyses, both controlling for demographics and smoking status (odds ratio 2.21, 95% confidence interval 1.98-2.47, P < .001) and with the addition of atopic comorbidities (odds ratio 2.38, 95% confidence interval 2.12-2.67, P < .001). The odds of eczema increased with greater PM2.5 concentration in this large, diverse, adult American cohort. Ambient air pollution is an environmental hazard that influences inflammatory skin disease, suggesting possible targeted interventions.

Exposure to ambient air pollutions and its association with adverse birth outcomes: a systematic review and meta-analysis of epidemiological studies

IntroductionAir pollution is a significant global public health concern. However, there is a lack of updated and comprehensive evidence regarding the association between exposure to ambient air pollution and adverse birth outcomes (preterm birth, low birth weight, and stillbirth). Furthermore, the existing evidence is highly inconsistent. Therefore, this study aims to estimate the overall association between ambient air pollution and adverse birth outcomes.MethodsIn this study, initially a total of 79,356 articles were identified. Finally, a total of 49 articles were included. We conducted compressive literature searches using various databases, including PubMed, Scientific Direct, HINARI, and Google Scholar. Data extraction was performed using Microsoft Excel, and the data were exported to STATA 17 software for analysis. We used the Joanna Briggs Institute’s quality appraisal tool to ensure the quality of the included studies. A random effects model was employed to estimate the pooled prevalence. Publication bias was assessed using funnel plots and Egger’s regression test.ResultsIn this study, the pooled prevalence of at least one adverse birth outcome was 7.69% (95% CI: 6.70–8.69), with high heterogeneity (I2 = 100%, p-value &amp;lt; 0.001). In this meta-analysis, high pooled prevalence was found in preterm birth (6.36%), followed by low birth weights (5.07%) and stillbirth (0.61%). Exposure to PM2.5 (≤10 μg/m3) throughout the entire pregnancy, PM2.5 (≤10 μg/m3) in the first trimester, PM10 (&amp;gt;10 μg/m3) during the entire pregnancy, and O3 (≤10 μg/m3) during the entire pregnancy increased the risk of preterm birth by 4% (OR = 1.04, 95% CI: 1.03–1.05), 5% (OR = 1.05, 95% CI: 1.01–1.09), 49% (OR = 1.49, 95% CI: 1.41–1.56), and 5% (OR = 1.05, 95% CI: 1.04–1.07), respectively. For low birth weight, exposure to PM2.5 (≤10 μg/m3) and PM2.5 (&amp;gt;10 μg/m3) throughout the entire pregnancy was associated with an increased risk of 13% (OR = 1.13, 95% CI: 1.05–1.21) and 28% (OR = 1.28, 95% CI: 1.23–1.33), respectively.ConclusionThis study highlighted a significant association between ambient air pollution and adverse birth outcomes. Therefore, it is crucial to implement a compressive public health intervention.Systematic review registrationThe review protocol was registered with the record ID of CRD42024578630.

Statistical and machine learning approaches for estimating pollution of fine particulate matter (PM2.5) in Vietnam

This study aims to predict fine particulate matter (PM2.5) pollution in Ho Chi Minh City, Vietnam, using autoregressive integrated moving average (ARIMA), linear regression (LR), random forest (RF), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and convolutional neural network (CNN) combining Bi-LSTM (CNN+Bi-LSTM). Two experiments were set up: the first one used data from 2018–2020 and 2021 as training and test data, respectively. Data from 2018–2021 and 2022 were used as training and test data for the second experiment, respectively. Consequently, ARIMA showed the worst performance, while CNN+Bi-LSTM achieved the best accuracy, with an R² of 0.70 and MAE, MSE, RMSE, and MAPE of 5.37, 65.4, 8.08 µg/m³, and 29%, respectively. Additionally, predicted air quality indexes (AQIs) of PM2.5 were matched the observed ones up to 96%, reflecting the application of predicted concentrations for AQI computation. Our study highlights the effectiveness of machine learning model in monitoring of air pollution.

Impact of air pollution prevention and control on urban green economy efficiency: evidence from China

Impact of air pollution prevention and control on urban green economy efficiency: evidence from China

Impact of air pollution on coronary artery disease: A qualitative study

Impact of air pollution on coronary artery disease: A qualitative study

Facility Compliance and Natural Disaster Events: Evidence From the U.S. Clean Air Act

With the increasing severity and frequency of natural disasters, we investigate the impact of such events on company environmental performance in the United States. This study analyzes the effect of natural disasters on facility compliance with the Clean Air Act throughout the sample period from 2013 to 2022. Our findings show that facilities located in disaster areas are associated with higher violation rates. By examining individual types of disasters separately, we find that the results are most pronounced for floods, fires, and severe storms. We suggest that regulators collectively evaluate the environmental policies and practices for companies in areas prone to natural disasters.

Rapid Air Quality Assessment and Modelling of an Educational Institute Located in a Coastal Region near National Highway

Rapid Air Quality Assessment and Modelling of an Educational Institute Located in a Coastal Region near National Highway

First direct measurements of the nitrogen isotopic composition of vehicle tailpipe-emitted NH3 and implications for the source apportionment of NH3 in urban atmosphere.

The introduction of three-way catalytic converter (TWC) to meet stringent vehicular NOx emission standards worldwide has led to an unintended consequence of vehicle-derived ammonia (NH3) emission, which might degrade air quality and affect human health, especially in urban areas. The nitrogen stable isotope composition of NH3 (δ15N-NH3) may be a useful tool to trace NH3 sources, but the isotopic signature of vehicle-emitted NH3 is lacking. Here we report the δ15N-NH3 measured from tailpipe exhausts collected directly from 19 different vehicles equipped with TWC using "grab" sample technique optimized to avoid isotopic fractionation. We found a large of range of NH3 concentration (from 1 to 37 ppm, x̄ ± 1σ = 10 ± 8 ppm) and δ15N value (from -38.1 ‰ to 49.0 ‰, x̄ ± 1σ = 1.2 ± 20.9 ‰) for our samples (n = 57). Our results indicate a correlation between δ15N-NH3 and NH3 concentrations (i.e. δ15N-NH3 (‰) = 16.9(± 2.2)In(NH3 concentration; ppm) - 31.7(± 4.7) (R2 = 0.53, P < 0.01) that is associated with the catalyst temperature, which is generally agree with the results of theoretical calculation. After consideration of the vehicle emission evolution, dominant driving condition and tunnel δ15N-NH3 variation, a representative δ15N-NH3 value (meanminmax) of vehicle source is identified as 0.8000-2.20005.2000, which is distinct from (larger than) other NH3 volatilization-related emission sources. Using our newly measured δ15N values and Bayesian mixing model, we approximate the overall contribution of vehicles to ambient NH3 before, during, and after an international event in Beijing was 14.1 %, 7.9 %, and 21.5 %, respectively, which agree well with the course of traffic restriction. Collectively, our findings demonstrate the importance of using vehicle-emitted δ15N-NH3 to quantify vehicular NH3 contribution in urban atmospheres.