Spatiotemporal assessment of air and noise pollution in railway environments using drone–LiDAR integration

Road traffic air and noise pollution exposure assessment – A review of tools and techniques

Road traffic contributes to air and noise pollution in urban areas, negatively impacting human health. Understanding exposure to air and noise pollution from road traffic is vital for epidemiological studies on human health. This paper aims to (i) summarize current modeling and assessment methods for road traffic-related air and noise pollution, (ii) emphasize the potential of existing tools and techniques for assessing combined air and noise exposure, and (iii) highlight associated challenges, research gaps, and priorities. The paper examines literature concerning air and noise pollution caused by urban road traffic, including dispersion models, Geographic Information System (GIS) tools, spatial exposure assessment scales, study locations, sample sizes, traffic data types, and building geometry information. Approximately 29% of accredited research parameters for air pollution utilized NO2, underscoring the significance of this element in the research context. Additionally, Lden was employed in nearly 34% of publication parameters for noise pollution. Deterministic modeling is the most commonly used technique for assessing short-term and long-term exposure to air and noise pollution. Among the models, more diversity is in air pollution models than in noise pollution models. Correlations between air and noise pollution vary widely and are influenced by numerous factors, such as traffic characteristics, building attributes, and meteorological conditions. Buildings serve as barriers to pollution dispersion, with a more significant reduction effect observed for noise pollution than for air pollution. Meteorology plays a greater role in influencing air pollution levels than noise pollution, although it is also essential for noise pollution assessment. There is considerable potential for developing a standardized tool to assess combined exposure to traffic-related air and noise pollution, facilitating health-related studies. With its geographic capabilities, GIS is well-established and well-suited to address air and noise pollution assessments simultaneously.

Background: A growing number of studies suggest that environmental noise pollution may impact the risk of hypertension, but the relationship during pregnancy is poorly understood. We investigated the association between environmental noise levels and preeclampsia, a hypertensive disorder of pregnancy.Methods: We undertook a retrospective cohort study of 269,263 pregnancies in Montreal, 2000-2013. Using postal codes, we assigned environmental noise pollution levels (dBA) from land use regression models to each pregnancy. We calculated odds ratios (OR) and 95% confidence intervals (CI) for the association of environmental noise with preeclampsia, adjusted for air pollutants, neighbourhood walkability, maternal age, parity, multiple pregnancy, comorbidity, socioeconomic deprivation, and year of delivery. We assessed if associations varied according to preeclampsia severity (mild and severe) and onset time (<34 and ≥34 weeks of gestation).Results: Women exposed to elevated environmental noise levels (≥65 vs. <50 dBA) had a higher prevalence of preeclampsia (37.9 vs. 27.9 per 1,000). Compared with 50 dBA, exposure to a noise level of 65 dBA was associated with 1.09 times the odds of preeclampsia (95% CI 0.99-1.20). Associations were stronger for severe preeclampsia (OR 1.29, 95% CI 1.09-1.54) and preeclampsia before 34 gestational weeks (OR 1.71, 95% CI 1.20-2.43). There was no association with mild preeclampsia and preeclampsia at ≥34 weeks.Conclusion: Environmental noise pollution may be a risk factor for preeclampsia, particularly severe or early onset preeclampsia. In light of rising levels of urban noise, these results suggest that vulnerable populations, including pregnant women, could benefit from residential noise reduction policies.

Higher prevalence of breathlessness in elderly exposed to indoor aldehydes and VOCs in a representative sample of French dwellings

Environmental noise is a major concern, particularly in the vicinity of hospitals, which are designated as sensitive areas. There are many complaints about the outside noise, which makes their time in the hospital uncomfortable. Numerous factors, such as expanding urbanisation, industrial activity, traffic, and building, contribute to environmental noise pollution. To avoid having a significant negative effect on users, it is crucial to investigate the sources and measure the level of environmental noise. To date, no data has been recorded on environmental noise around public hospitals in Malaysia. The aim of this study is to assess the current environmental noise pollution surrounding selected hospitals and explore potential improvements that contribute to future urban planning. This study integrates a field measurement at three public hospitals in the Klang Valley (Hospital Shah Alam, Hospital Tengku Ampuan Rahimah and Hospital Sungai Buloh), employing quantitative data collection via a sound level meter with a data logger to identify the various environmental noise sources surrounding public hospitals in the Klang Valley. The findings indicate that in one case study, the average readings failed to meet the DOE standard, categorising it as environmental noise pollution. Considering the results obtained, all three case studies’ environments require significant improvements that can be addressed through strategic urban planning, such as enforcing zoning regulations that restrict noise-emitting activities in the surrounding areas.

A combined assessment of air and noise pollution on the High Line, New York City

- Introduction. - Monitoring and Assessment of Air Pollution. - Air Pollution Modeling. - Impact of Air Pollutants on Plant Metabolism and Antioxidant Machinery. - Role of Global Climate Change in Crop Yield Reductions. - Air Pollution and Its Role in Stress Physiology. - Air Pollution Exposure Studies Related to Human Health. - Impacts of Air Pollution on Epidemiology and Cardiovascular Systems. - Health Risk Assessment and Management of Air Pollutants. - Air Quality Management Practices: A Sustainable Perspective.

Assessment of an air pollution monitoring network to generate urban air pollution maps using Shannon information index, fuzzy overlay, and Dempster-Shafer theory, A case study: Tehran, Iran

Pathways linking residential noise and air pollution to mental ill-health in young adults

We designed this study to map environmental noise pollution (ENP) around all elementary schools and kindergartens in Tehran using a land use regression (LUR) approach. Out of 135 spatial predictor variables, seven were identified as significant determinants of ENP. The final model demonstrated strong predictive performance, with an R² of 0.70 and an adjusted R² of 0.65. Additionally, the model had a leave-one-out cross-validation (LOOCV) R² of 0.59 and a root mean squared error (RMSE) of 3.15, indicating acceptable predictive accuracy. Among the significant predictors, green space area, and the distances to the nearest terminals, primary roads, and highways had negative effects on ENP, meaning that increases in these variables reduce noise levels around schools and kindergartens. In contrast, the length of secondary roads, the area of commercial parcels, and the distance to military zones had positive effects, suggesting that increases in these variables contributed to higher ENP. Our findings reveal substantial spatial variation in environmental noise levels across Tehran, with the highest ENP values—ranging from 65.1 dB(A) to 85 dB(A)—concentrated primarily in the central, southern, and southeastern districts of the city. Approximately 36%, 30%, and 13% of educational institutions for children in Tehran are exposed to ENP in the range of 70.1–75 dB(A), 65–70 dB(A), and > 75 dB(A), respectively. Only 4% of these institutions are located in areas with ENP < 60 dB(A). Our findings highlight the importance of infrastructure design changes, such as expanding green spaces around schools and kindergartens, or relocating these educational institutions farther from terminals, primary roads, military zones, and commercial areas.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-21249-2.

Background: One of the key indicators of the degradation of the environment is the noise level. This has necessitated this study on the evaluation of the public, perceptional awareness, sources, effects, and mitigation measures on environmental noise pollution. Methods: The population was estimated and 385 structured questionnaires were estimated and administered by random purposive sampling. About 358 questionnaires were retrieved. Data were analyzed using SPSS and Excel statistical software. Results: About 90.2% of the respondents had relevant awareness and its effects on environmental noise while 9.8% of the respondent did not. Traffic, generators, commercial and light industry sources of noise, and their severity were ranked in a descending order using the Likert scale. Hearing impairment, annoyance, stress, distraction during exposure were ranked in a descending order using the Likert scale. Single-factor ANOVA on the sources of noise and their severity, awareness of the various effects of noise, and responses during exposure showed that there were significant differences as P&lt;0.05 using a confidence level of 95%. About 61.7% of respondents complained of environmental noise, 72.6% respondents received complaints about environmental noise, 87.7% of respondents were not aware of any government agency monitoring noise pollution, 72.2% of the respondents had done nothing regarding noise prevention, and 91.1% respondents wanted a proactive decision in mitigating environmental noise pollution. Conclusion: There is an inadequate coping strategy. Strategic planning in mitigating environmental noise in urban and semi-urban areas is a necessity and there is a need for public enlightenment by government monitoring agencies.

IntroductionAir and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation...

Environmental noise pollution and risk of preeclampsia

There is evidence that hypertensive heart disease is attributed to environmental noise and air pollution in European regions. Epidemiological studies have also demonstrated the potential role of road traffic air–noise pollution in adverse health outcomes, including cardiovascular diseases such as hypertension. Despite the local implementation of the EU Directive on environmental noise and air quality, it is necessary to explore the progress and understand the impact of policy, legislation and the collection of exposure and associated health data for air and noise pollution in order to improve environmental public health. Therefore, the DPSEEA (Driving force, Pressure, State, Exposure, Effect and Action) conceptual framework model was used to systematically map and review these links and to identify relevant indicators linking air–noise pollution with cardiovascular diseases. With a focus on the EU and specifically UK situation, we critically evaluate the effectiveness of evidence-based policy implementation of action plans, summarizing existing data using modified framework model tools. We concluded that, the DPSEEA conceptual framework provides an effective review method to more effectively, conduct data surveillance monitoring and assessment, and tracking outcomes with different types of evidence in the field of environmental public health. There is great scope demonstrating the use of the DPSEEA conceptual framework to highlight the casual relationship between exposure and effects taking into account other factors such as driving force, pressure, state, exposure and action and to incorporate as surveillance information in the environmental health tracking system (EHTS).

Large-scale assessment of atmospheric air pollution by mercury (Hg) using lichen Parmelia caperata as biological indicator was undertaken using samples from five provinces of South Africa collected between 2013 and 2017. Analysis of lichens provides time-integrated data, which correspond to the mean Hg concentration in air at a specific location over a long time period. Determination of Hg in lichens was carried out by direct thermal decomposition of samples using a Zeeman-effect atomic absorption spectrometer, thereby requiring no chemical pretreatment. The lowest mercury concentration of 60 ± 8.0ngg-1 (n = 45) was measured in lichens from Limpopo province. This value was accepted as a background Hg concentration in SA lichens. The Hg in lichens from northern parts of Mpumalanga province varied from 72 ± 9.0 to 100 ± 17ngg-1 (n = 45), while in southern parts of the province, where 11 coal-fired electrical power stations are located, values ranged from 139 ± 7.0 to 183 ± 10ngg-1 (n = 28). The highest Hg concentration, 218 ± 21ngg-1 (n = 10), was found in lichens from Secunda, Mpumalanga province. It could be traced to the possible Hg emission during thermal treatment of coal at the largest SA industrial plant that transforms coal into liquid fuels. In Pretoria and Johannesburg, cities in Gauteng province, Hg in lichens was between 110 and 162ngg-1 (n = 48). Based on the results of measurements, the equation connecting Hg concentration in lichens with Hg concentration in air has been derived. It was used for the calculation of atmospheric Hg concentration in South African provinces. Calculated values (0.8-1.45ngm-3) were found to be within statistical summary of mean atmospheric Hg in remote places (1.70 ± 0.17ngm-3), and in other locations (1.5-3.0ngm-3) lower than in impacted areas of the world (5.20 ± 3.47ngm-3).