Evidence has shown associations between air pollution and traffic-related exposure with accelerated aging, but no study to date has linked the exposure with age at natural menopause, an important indicator of reproductive aging. In this study, we sought to examine the associations of residential exposure to ambient particulate matter (PM) and distance to major roadways with age at natural menopause in the Nurses’ Health Study II (NHS II), a large, prospective female cohort in US. A total of 105,996 premenopausal participants in NHS II were included at age 40 and followed through 2015. Time-varying residential exposures to PM₁₀, PM₂.₅₋₁₀, and PM₂.₅ and distance to roads was estimated. We calculated hazard ratios (HR) and 95% confidence intervals (CIs) for natural menopause using Cox proportional hazard models adjusting for potential confounders and predictors of age at menopause. We also examined effect modification by region, smoking, body mass, physical activity, menstrual cycle length, and population density. There were 64,340 reports of natural menopause throughout 1,059,229 person-years of follow-up. In fully adjusted models, a 10 μg/m³ increase in the cumulative average exposure to PM₁₀ (HR: 1.02, 95% CI: 1.00, 1.04), PM₂.₅₋₁₀ (HR: 1.03, 95% CI: 1.00, 1.05), and PM₂.₅ (HR: 1.03, 95% CI: 1.00, 1.06) and living within 50 m to a major road at age 40 (HR: 1.03, 95%CI: 1.00, 1.06) were associated with slightly earlier menopause. No statistically significant effect modification was found, although the associations of PM were slightly stronger for women who lived in the West and for never smokers. To conclude, we found exposure to ambient PM and traffic in midlife was associated with slightly earlier onset of natural menopause. Our results support previous evidence that exposure to air pollution and traffic may accelerate reproductive aging.

The construction of noise maps is of great significance for the development of urban sustainability and the protection of residents’ physical and mental health. The traditional noise map construction method is difficult to be widely used because of its low update frequency and high drawing cost. Based on the crowd-sensing technology and Latent Factor Model (LFM), this paper proposes a new noise map completion method called Spatial-Temporally Related LFM (STR-LFM) for solving the problem of data sparseness. First, the geographic information features including Point of Interest (POI), road network and building outline are fully excavated, and then combine the correlation of the samples in the time dimension to construct the similarity matrixes. After that, use the k-nearest neighbor algorithm to find out the similar samples of missing positions, and finally regard their weighted fusion as the predicted values. Experimental results show that the recovery error is lower than other commonly used methods, and the proposed method has better stability when faced with data sparseness problems at different levels.

Ambient fine particulate matter (PM₂.₅) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM₂.₅ spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM₂.₅. Daily average PM₂.₅ data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM₂.₅ variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM₂.₅ exposures.

Microplastic pollution has become an emergency issue in the global environment. However, little is known about the occurrence and distribution of microplastics in agroecological system. In this study, we investigated the pollution of microplastics in vegetable farmlands in suburb of Wuhan, central China. Results showed that the abundance of microplastics ranged from 320 to 12,560 items/kgdw. Microplastic pollution adjacent to the suburban roads was about 1.8 times as serious as that in the residential areas. Microplastics with size less than 0.2 mm were dominated, reaching 70% in total. The main types of microplastics were fibers and microbeads. Moreover, polyamide (32.5%) and polypropylene (28.8%) were the main types of polymer. This study proclaims the occurrence and characteristics of microplastic pollution in typical farmland soils of suburb land. It may provide significant basis for subsequent research about microplastics contaminant in the terrestrial ecosystem.

Increasing levels of road noise are creating new anthropogenic soundscapes that may affect wildlife globally. Bats, which form about a third of all mammal species, are sensitive bioindicators, and may be particularly vulnerable because of their dependency on echolocation. Here we present the first controlled field experiment with free-living bats. Using a Before-After-Control-Impact phantom road experimental design, we examine the impacts of traffic noise on their activity and feeding behaviour. Disentangling the impacts of traffic noise from other co-varying exposures such as habitat quality, the experiment demonstrates a significant negative effect on the activity of each of the five, ecologically different, species (genus for Myotis spp.) examined. This suggests that the results are widely applicable. The negative effects are largely attributable to noise in the sonic spectrum, which elicited aversive responses in all bat species tested,whereas responses to ultrasoundwere restricted to a single species. Our findings demonstrate that traffic noise can affect bat activity at least 20m away from the noise source. For Pipistrellus pipistrellus and Pipistrellus pygmaeus, feeding behaviour, as well as overall activity, was negatively affected. Ecological Impact Assessments are therfore needed wherever there are significant increases in traffic flow, and not just when new roads are built. Further research is required to identify effective mitigation strategies, to delineate the zone of influence of road noise, and to assess whether there is any habituation over time.

Air pollution is one of the most important branches of environmental science as it affects human health, climate and ecosystems. Emissions of air pollutants from transport (vehicles and ships) in port cities strongly affect air quality at local scales, warranting for a combination of theoretical and experimental studies to identify pollution hotspots. The purpose of this paper is to provide a methodology for developing a hybrid emission inventory from transport sector for two port cities located respectively on the Northern Aegean islands of Chios and Lesvos. Emission inventories were constructed for the year 2014 based on top-down and bottom-up approaches. Official data from local authorities and survey results were used for the calculation of emissions. Traffic emissions were spatially allocated to the road network based on population data and hourly traffic counts, and distributed over time (on an hourly basis) with the use of local temporal coefficients.Regarding carbon monoxide road emissions, the highest quantities are mainly emitted by Passenger Cars (43%,32% in Chios and Lesvos respectively) while for PM₁₀ emissions, trucks have the largest share (66% in Chios and 86% in Lesvos). The pollutants that are emitted in greater quantities from the ships at the ports of Mytilene and Chios are NOₓ, followed by SO₂ and CO. Most of the ship emissions in the ports occur by the ships at berth, as they remain berthed for hours whereas maneuvering lasts 15–20 min. As for the daily contribution of the two transport sources to the pollution profile of Mytilene, road emissions are higher for almost all pollutants. However, the contribution of ship emissions is not negligible, especially during the touristic period when marine traffic increases and emissions close to the port area become more important than those from road transport.

Adverse health effects of exposure to air pollution have been investigated in many previous studies. However, there is no study available on the association between maternal exposure to air pollution during pregnancy and cord blood lipid profile. This study, based on 150 mother-newborn pairs residing in Sabzevar, Iran (2018), evaluated the association of exposure to ambient air pollution as well as traffic indicators (total street length in different buffers around residential address and distance to major roads) during entire pregnancy with lipid levels cord blood lipid profile. Concentrations of PM₁₀, PM₂.₅, and PM₁ at maternal residential address were estimated using land use regression (LUR) models. We measured triglyceride (TAG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) levels and TC/HDL-C and TAG/HDL-C ratio in the cord blood samples to characterize their lipid profile. Multiple linear regression models were developed to estimate the association of exposure to air pollution and traffic indicators with cord blood lipid profile controlled for relevant covariates. Higher concentrations of PM₂.₅ and PM₁₀ were associated with higher levels of TAG, TC, HDL-C, TC/HDL-C, and TAG/HDL-C in cord blood samples. Moreover, higher concentration of PM₁ was associated with higher levels of TAG, TC and LDL-C. There was also a positive association between total street length in 100 m buffer around home and serum levels of TC, TAG, LDL-C and TC/HDL ratio (β = 3.73, 95% confidence intervals (CI): 1.76, 5.71; β = 2.75, 95% CI: 0.97, 4.53; β = 1.87, 95% CI: 0.64, 3.09; β = 0.06, 95% CI: 0.01, 0.11, respectively). However, the associations for total street length in larger buffers and distance to major roads were not statistically significant. Our findings support a relationship between exposure to air pollution during pregnancy and increase in cord blood lipid levels.

The consequences of indoor and outdoor air pollution on human health are of great concern nowadays. In this study, we firstly evaluated indoor and outdoor air pollution levels (CO, CO₂, NO, NO₂, PM₁₀) at an urban site in Dakar city center and at a rural site. Then, the individual exposure levels to selected pollutants and the variations in the levels of biomarkers of exposure were investigated in different groups of persons (bus drivers, traders working along the main roads and housemaids). Benzene exposure levels were higher for housemaids than for bus drivers and traders. High indoor exposure to benzene is probably due to cooking habits (cooking with charcoal), local practices (burning of incense), the use of cleaning products or solvent products which are important emitters of this compound. These results are confirmed by the values of S-PMA, which were higher in housemaids group compared to the others. Urinary 1-HOP levels were significantly higher for urban site housemaids compared to semirural district ones.Moreover, urinary levels of DNA oxidative stress damage (8-OHdG) and inflammatory (interleukin-6 and -8) biomarkers were higher in urban subjects in comparison to rural ones.The air quality measurement campaign showed that the bus interior was more polluted with PM₁₀, CO, CO₂ and NO than the market and urban or rural households. However, the interior of households showed higher concentration of VOCs than outdoor sites confirming previous observations of higher indoor individual exposure level to specific classes of pollutants.

In the study, personal PM₂.₅ exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM₂.₅ monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM₂.₅ data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM₂.₅ exposures and produced poor correlations between personal and ambient PM₂.₅. These results demonstrate that individual behavior strongly affects personal PM₂.₅ exposure. Six primary sources of personal PM₂.₅ exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM₂.₅ exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

Stormwater is viewed as an alternative resource to mitigate water shortages. However, stormwater reuse is constrained due to the presence of many toxic pollutants such as hydrocarbons. Effective mitigation requires robust mathematical models for stormwater quality prediction based on an understanding of pollutant processes. However, the rise in global temperatures will impose changes to pollutant processes. This study has proposed a new perspective on modelling the build-up process of hydrocarbons, with a focus on volatile organic compounds (VOCs). Among organic compounds, VOCs are the most susceptible to changes as a result of global warming due to their volatility. Seven VOCs, namely, benzene, toluene, ethylbenzene, para-xylene, meta-xylene, ortho-xylene and styrene in road dust were investigated. The outcomes are expected to lay the foundation to overcoming the limitations in current modelling approaches such as not considering the influence of temperature and volatility, on the build-up process. A new conceptualisation is proposed for the classical build-up model by mathematically defining the volatility of VOCs in terms of temperature. Uncertainty in the re-conceptualised build-up model was quantified and was used to understand the build-up patterns in the future scenarios of global warming. Results indicated that for the likely scenarios, the variability in VOCs build-up gradually increases at the beginning of the dry period and then rapidly increases after around seven days, while the build-up reaches a near-constant value in a shorter dry period, limiting the variability. These initial research outcomes need to be further investigated given the expected impacts of global warming into the future.