Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems

Examining the relationship between land use and childhood leukemia and lymphoma in Tehran

We conducted a hospital-based case-control study to explore the association between proximity to various land use types and childhood leukemia and lymphoma. This research involved 428 cases of childhood leukemia and lymphoma (2016-2021), along with a control group of 428 children aged 1-15 in Tehran. We analyzed the risk of childhood cancer associated with land use by employing logistic regression adjusted for confounding factors such as parental smoking and family history. The odds ratio (OR) for children with leukemia and lymphoma residing within 100 m of the nearest highway was 1.87 (95% CI = 1.00-3.49) and 1.71 (95% CI = 1.00-2.93), respectively, in comparison to those living at a distance of 1000 m or more from a highway. The OR for leukemia with exposure to petrol stations within 100 m was 2.15 (95% CI = 1.00-4.63), and for lymphoma it was 1.09 (95% CI = 0.47-2.50). A significant association was observed near power lines (OR = 3.05; 95% CI = 0.97-9.55) within < 100 m for leukemia. However, no significant association was observed between power lines and the incidence of childhood lymphoma. There was no association between bus stations, major road class 2, and the incidence of childhood leukemia and lymphoma. In conclusion, our results suggest a possible association between the incidence of childhood leukemia and proximity to different urban land uses (i.e., highways and petrol stations). This study is the first step in understanding how urban land use affects childhood leukemia and lymphoma in Tehran. However, comprehensive studies considering individual-level data and specific pollutants are essential for a more nuanced understanding of these associations.

Quantifying the contribution of environmental variables to cyclists' exposure to PM2.5 using machine learning techniques

Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM2.5 predictions, with a minimum root mean square error value of 5.62 μg m-3. The variables with greatest influence on cyclist exposure were the temporal ones (month, day of the week, and time of the day), followed by meteorological variables, such as temperature, relative humidity, wind speed, wind direction, and atmospheric pressure. Additionally, we considered relevant attributes, which are partially linked to spatial characteristics. These attributes encompass street typology, vegetation density, and the flow of vehicles on a particular street, which quantifies the number of vehicles passing a given point per minute. Mean PM2.5 concentration was lower in bicycle paths away from vehicular traffic than in bike lanes along streets. These outcomes underscore the need to thoughtfully design public transportation routes, including bus routes, concerning the network of bicycle pathways. Such strategic planning attempts to improve the air quality in urban landscapes.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Association of Air Pollution with a Urinary Biomarker of Biological Aging and Effect Modification by Vitamin K in the FLEMENGHO Prospective Population Study

BACKGROUND

A recently developed urinary peptidomics biological aging clock can be used to study accelerated human aging. From 1990 to 2019, exposure to airborne particulate matter (PM) became the leading environmental risk factor worldwide.

OBJECTIVES

This study investigated whether air pollution exposure is associated with accelerated urinary peptidomic aging, independent of calendar age, and whether this association is modified by other risk factors.

METHODS

In a Flemish population, the urinary peptidomic profile (UPP) age (UPP-age) was derived from the urinary peptidomic profile measured by capillary electrophoresis coupled with mass spectrometry. UPP-age-R was calculated as the residual of the regression of UPP-age on chronological age, which reflects accelerated aging predicted by UPP-age, independent of chronological age. A high-resolution spatial-temporal interpolation method was used to assess each individual's exposure to PM 10 , PM 2.5 , black carbon (BC), and nitrogen dioxide (NO 2 ). Associations of UPP-age-R with these pollutants were investigated by mixed models, accounting for clustering by residential address and confounders. Effect modifiers of the associations between UPP-age-R and air pollutants that included 18 factors reflecting vascular function, renal function, insulin resistance, lipid metabolism, or inflammation were evaluated. Direct and indirect (via UPP-age-R) effects of air pollution on mortality were evaluated by multivariable-adjusted Cox models.

RESULTS

Among 660 participants (50.2% women; mean age: 50.7 y), higher exposure to PM 10 , PM 2.5 , BC, and NO 2 was associated with a higher UPP-age-R. Studying effect modifiers showed that higher plasma levels of desphospho-uncarboxylated matrix Gla protein (dpucMGP), signifying poorer vitamin K status, steepened the slopes of UPP-age-R on the air pollutants. In further analyses among participants with dpucMGP ≥ 4.26 μ g / L (median), an interquartile range (IQR) higher level in PM 10 , PM 2.5 , BC, and NO 2 was associated with a higher UPP-age-R of 2.03 [95% confidence interval (CI): 0.60, 3.46], 2.22 (95% CI: 0.71, 3.74), 2.00 (95% CI: 0.56, 3.43), and 2.09 (95% CI: 0.77, 3.41) y, respectively. UPP-age-R was an indirect mediator of the associations of mortality with the air pollutants [multivariable-adjusted hazard ratios from 1.094 (95% CI: 1.000, 1.196) to 1.110 (95% CI: 1.007, 1.224)] in participants with a high dpucMGP, whereas no direct associations were observed.

DISCUSSION

Ambient air pollution was associated with accelerated urinary peptidomics aging, and high vitamin K status showed a potential protective effect in this population. Current guidelines are insufficient to decrease the adverse health effects of airborne pollutants, including healthy aging trajectories. https://doi.org/10.1289/EHP13414.

The effect of exposure to traffic related air pollutants in pregnancy on birth anthropometry: a cohort study in a heavily polluted low-middle income country

BACKGROUND

Ambient air pollution has been recognized as one of the most important environmental health threats. Exposure in early life may affect pregnancy outcomes and the health of the offspring. The main objective of our study was to assess the association between prenatal exposure to traffic related air pollutants during pregnancy on birth weight and length. Second, to evaluate the association between prenatal exposure to traffic related air pollutants and the risk of low birth weight (LBW).

METHODS

Three hundred forty mother-infant pairs were included in this prospective cohort study performed in Jakarta, March 2016-September 2020. Exposure to outdoor PM_2.5, soot, NO_x, and NO₂ was assessed by land use regression (LUR) models at individual level. Multiple linear regression models were built to evaluate the association between air pollutants with birth weight (BW) and birth length (BL). Logistic regression was used to assess the risk of low birth weight (LBW) associated with all air pollutants.

RESULTS

The average PM_2.5 concentration was almost eight times higher than the current WHO guideline and the NO₂ level was three times higher. Soot and NO_x were significantly associated with reduced birth length. Birth length was reduced by - 3.83 mm (95% CI -6.91; - 0.75) for every IQR (0.74 × 10^- 5 per m) increase of soot, and reduced by - 2.82 mm (95% CI -5.33;-0.30) for every IQR (4.68 μg/m³) increase of NO_x. Outdoor air pollutants were not significantly associated with reduced birth weight nor the risk of LBW.

CONCLUSION

Exposure to soot and NO_x during pregnancy was associated with reduced birth length. Associations between exposure to all air pollutants with birth weight and the risk of LBW were less convincing.

Size fraction of hazardous particulate matter governing the respiratory deposition and inhalation risk in the highly polluted city Delhi

Delhi has been identified as one of the highly polluted cities in the world and recently associated with the highest population weighted PM_2.5 concentration. However, the unavailability of the health risk estimations using long-term data for Indian cities has been pointed out as a hurdle in performing the correct assessment. The present work estimated deposition of particles in different regions of respiratory systems (head airway = 67% deposition for 2.5 µm particles; tracheo-bronchiolar (TB) = 73% deposition for 1.0 µm particles; alveolar (AL) = 17% deposition for 0.5 µm, 0.25 µm, and < 0.25 µm particles) using PM samples collected at a breathing height of 1.5 m near the major ring road in New Delhi (India). The calculated risk index (RI) varied considerably between winter (1.21 ± 0.26 to 1.33 ± 0.50) and pre-monsoon-southwest monsoon months (0.34 ± 0.08 to 0.96 ± 0.27). Respiratory deposition dose of nanosized particles (≤ 500 nm) in the alveoli region of the lung was found to be considerable (35%) indicating the need for understanding the role of these particles in posing health risk. Although the calculated values of risk metric for exposures of PM-associated metals indicated no risk to IIT Delhi population (hazard quotient < 1 and excess risk of getting cancer < 10^-6-10^-9), continuous monitoring for particles of different sizes at inhalation height are required for protecting human health.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study

BACKGROUND

Recent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people's homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed.

OBJECTIVE

This simulation study aimed to understand the impact of incomplete addresses on health effect estimation based on the association between particulate matter with diameter ≤10 µm (PM₁₀) and low birth weight (LBW).

METHODS

We generated true annual average concentrations of PM₁₀ at 46,007 mothers' homes and their LBW status, using the parameters obtained from our data analysis and a previous study in Seoul, Korea. Then, we hypothesized that mothers' address information is limited to the district and compared the properties of their health effect estimates of PM₁₀ with those using complete addresses. We performed this comparison across eight environmental scenarios that represent various spatial distributions of PM₁₀ and nine exposure prediction methods that provide different sets of predicted PM₁₀ concentrations of mothers.

RESULTS

We observed increased bias and root mean square error consistently across all environmental scenarios and prediction methods using incomplete addresses compared to complete addresses. However, the bias related to incomplete addresses decreased when we used population-representative exposures averaged to the district from predicted PM₁₀ at census tract centroids.

SIGNIFICANCE

Our simulation study suggested that individual exposure estimated by prediction approaches and averaged across population-representative points can provide improved accuracy in health effect estimates when complete address data are unavailable.

IMPACT STATEMENT

Our simulation study focused on a common and practical challenge of limited address information in air pollution epidemiology, and investigated its impact on health effect analysis. Cohort studies of air pollution have developed advanced exposure prediction model to allow the estimation of individual-level long-term air pollution concentrations at people's addresses. However, it is common that address information of existing health data is available at the coarse spatial scale such as city, district, and zip code area. Our findings can help understand the possible consequences of limited address information and provide practical guidance in achieving the accuracy in health effect analysis.

On the Exploitation of Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions

Remote sensing technologies are extensively applied to prevent, monitor, and forecast hazardous risk conditions in the present-day global climate change era. This paper presents an overview of the current stage of remote sensing approaches employed to study coastal and delta river regions. The advantages and limitations of Earth Observation technology in characterizing the effects of climate variations on coastal environments are also presented. The role of the constellations of satellite sensors for Earth Observation, collecting helpful information on the Earth’s system and its temporal changes, is emphasized. For some key technologies, the principal characteristics of the processing chains adopted to obtain from the collected raw data added-value products are summarized. Emphasis is put on studying various disaster risks that affect coastal and megacity areas, where heterogeneous and interlinked hazard conditions can severely affect the population.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Land-Use Regression Modeling to Estimate NO2 and VOC Concentrations in Pohang City, South Korea

Land-use regression (LUR) has emerged as a promising technique for air pollution modeling to obtain the spatial distribution of air pollutants for epidemiological studies. LUR uses traffic, geographic, and monitoring data to develop regression models and then predict the concentration of air pollutants in the same area. To identify the spatial distribution of nitrogen dioxide (NO2), benzene, toluene, and m-p-xylene, we developed LUR models in Pohang City, one of the largest industrialized areas in Korea. Passive samplings were conducted during two 2-week integrated sampling periods in September 2010 and March 2011, at 50 sampling locations. For LUR model development, predictor variables were calculated based on land use, road lengths, point sources, satellite remote sensing, and population density. The averaged mean concentrations of NO2, benzene, toluene, and m-p-xylene were 28.4 µg/m3, 2.40 µg/m3, 15.36 µg/m3, and 0.21 µg/m3, respectively. In terms of model-based R2 values, the model for NO2 included four independent variables, showing R2 = 0.65. While the benzene and m-p-xylene models showed the same R2 values (0.43), toluene showed a lower R2 value (0.35). We estimated long-term concentrations of NO2 and VOCs at 167,057 addresses in Pohang. Our study could hold particular promise in an epidemiological setting having significant health effects associated with small area variations and encourage the extended study using LUR modeling in Asia.

Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China

The intraurban distribution of PM_2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM_2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM_2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R² compared with cross-validation results. For MLR models, inflation of both R² and R²_CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_2.5 levels. Inflated within-sample R² also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM_2.5 concentrations.

Time series analysis and spatial distribution map of aggregate risk index due to tropospheric NO2 and O3 based on satellite observation

A high increase in human activities has led to more emission of air pollutants in metropolises and industrial areas. Recently, remotely sensed data of tropospheric pollutants is used for environmental management and decision-making on large scale. The purpose of this study was a time series analysis of nitrogen dioxide Vertical Column Density (NO₂ VCD) and Ozone (O₃) using Ozone Monitoring Instrument (OMI) from 2005 to 2016 by Mann-Kendall test. Also, the aggregate risk index (ARI) was calculated to estimate the overall impact of exposure to tropospheric NO₂ and O₃ concentrations at the national scale in 2016. To estimate the surface NO₂ related drivers, The Radial Basis Function (RBF) neural network modeling was performed for different months of 2016. Results of Mann-Kendall test showed that tropospheric ozone concentration had an increasing trend in all parts of Iran and this increasing trend was significantly higher in the southern region of Iran and lower in the northern parts of Iran. NO₂ VCD in most parts of Iran had a significant increasing trend. The result of sensitivity analysis showed that NO₂ VCD (1.25), the distance to the industrial area, (1.20) and wind speed (1.07) were the most important variables for the estimation of surface NO₂ concentration. Spatial ARI with the highest risks is mainly located in the Northern half of Iran, especially in Tehran, Alborz, and Khorasan-e- Razavi provinces, where NO₂ and O₃ concentrations are very severe. In northern Iran and central cities, the ARI values are calculated from 1.5 to 2.08, indicating the highest human health risks in these regions. The human health risks based on OMI observation were obtained higher in comparison to AQM data because the satellite data coverage is larger than AQM station and monitors transmitted air pollution by the wind in addition to local pollution. Based on this research, using satellite observation for air quality monitoring is a suitable tool for environmental management on a national scale.

Air Pollution and Environmental Justice in Latin America: Where Are We and How Can We Move Forward?

PURPOSE OF REVIEW

Air pollution in Latin America is a major environmental threat, yet few studies have focused on aspects of environmental justice with regard to air pollution in the region. We examined the scientific literature and described whether and how this issue has been addressed, identify possible gaps in knowledge, and offer suggestions for future research to contribute to policies that seek greater equity concerning air pollution impacts in Latin America.

RECENT FINDINGS

There is a limited literature that has addressed issues of environmental justice or environmental health inequalities about air pollution in Latin America, with studies concentrated in Brazil, Mexico, and Chile. Studies that examined disparities in exposure to air pollution found a clear pattern of higher exposure in socially deprived areas. Studies that examined disparities in health impacts associated with air pollution have mixed results, but many found a clear modification of effect with those in the lower socioeconomic groups presenting greater effects. Despite Latin America's colonial and slavery history, no studies have considered ethnicity or minority populations. The literature shows that health risks (exposure and susceptibility) associated with air pollution are unevenly distributed among Latin American populations. Methodological approaches varied and can be improved in future studies, especially for exposure assessment to air pollution, as well as for assigning socioeconomic position to individuals. Using smaller geographic units and spatial regression techniques will allow a reduction in measurement error. Attempts should be made to include both individual and contextual socioeconomic indicators in the analysis. Better quality information will help understand these differential exposures and effects and provide inputs to policies to tackle these inequalities.

Predicting intraurban airborne PM1.0-trace elements in a port city: Land use regression by ordinary least squares and a machine learning algorithm

Airborne particulate matter (PM) has been associated with cardiovascular and respiratory morbidity and mortality, and there is some evidence that spatially varying metals found in PM may contribute to adverse health effects. We developed spatially refined models for PM trace elements using ordinary least squares land use regression (OLS-LUR) and machine leaning random forest land-use regression (RF-LUR). Two-week integrated measurements of PM_1.0 (median aerodiameter < 1.0 μm) were collected at 50 sampling sites during fall (2010), winter (2011), and summer (2011) in the Halifax Regional Municipality, Nova Scotia, Canada. PM_1.0 filters were analyzed for metals and trace elements using inductively coupled plasma-mass spectrometry. OLS- and RF-LUR models were developed for approximately 30 PM_1.0 trace elements in each season. Model predictors included industrial, commercial, and institutional/ government/ military land use, roadways, shipping, other transportation sources, and wind rose information. RF generated more accurate models than OLS for most trace elements based on 5-fold cross validation. On average, summer models had the highest cross validation R² (OLS-LUR = 0.40, RF-LUR = 0.46), while fall had the lowest (OLS-LUR = 0.27, RF-LUR = 0.31). Many OLS-LUR models displayed overprediction in the final exposure surface. In contrast, RF-LUR models did not exhibit overpredictions. Taking overpredictions and cross validation performances into account, OLS-LUR performed better than RF-LUR in roughly 20% of the seasonal trace element models. RF-LUR models provided more interpretable predictors in most cases. Seasonal predictors varied, likely due to differences in seasonal distribution of trace elements related to source activity, and meteorology.

DEP-induced ZEB2 promotes nasal polyp formation via epithelial-to-mesenchymal transition

BACKGROUND

Diesel exhaust particles (DEPs) are associated with the prevalence and exacerbation of allergic respiratory diseases, including allergic rhinitis and allergic asthma. However, DEP-induced mechanistic pathways promoting upper airway disease and their clinical implications remain unclear.

OBJECTIVE

We sought to investigate the mechanisms by which DEP exposure contributes to nasal polyposis using human-derived epithelial cells and a murine nasal polyp (NP) model.

METHODS

Gene set enrichment and weighted gene coexpression network analyses were performed. Cytotoxicity, epithelial-to-mesenchymal transition (EMT) markers, and nasal polyposis were assessed. Effects of DEP exposure on EMT were determined using epithelial cells from normal people or patients with chronic rhinosinusitis with or without NPs. BALB/c mice were exposed to DEP through either a nose-only exposure system or nasal instillation, with or without house dust mite, followed by zinc finger E-box-binding homeobox (ZEB)2 small hairpin RNA delivery.

RESULTS

Bioinformatics analyses revealed that DEP exposure triggered EMT features in airway epithelial cells. Similarly, DEP-exposed human nasal epithelial cells exhibited EMT characteristics, which were dependent on ZEB2 expression. Human nasal epithelial cells derived from patients with chronic rhinosinusitis presented more prominent EMT features after DEP treatment, when compared with those from control subjects and patients with NPs. Coexposure to DEP and house dust mite synergistically increased the number of NPs, epithelial disruptions, and ZEB2 expression. Most importantly, ZEB2 inhibition prevented DEP-induced EMT, thereby alleviating NP formation in mice.

CONCLUSIONS

Our data show that DEP facilitated NP formation, possibly via the promotion of ZEB2-induced EMT. ZEB2 may be a therapeutic target for DEP-induced epithelial damage and related airway diseases, including NPs.

Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods

BACKGROUND

The gram pod borer, Helicoverpa armigera (Lepidoptera: Noctuidae) is an economically important pest of pigeonpea crop in India. Fixed plot surveys for H. armigera larvae were carried out at 28 pigeonpea fields located in the Southern Plateau and Hills agro-climatic zone of India for three crop seasons (nine sampling weeks per season). The spatiotemporal dynamics of H. armigera larvae in the experimental area (=Hanamkonda) was analysed using geostatistics tools, namely a variogram and Voronoi diagram, and H. armigera larval distribution patterns were further characterized and mapped.

RESULTS

A significant difference in H. armigera larval incidence was noticed between sampling weeks, with greater larval incidence observed between 26 November and 2 December. Pod formation phenophase (Meteorological Standard Week 44) of pigeonpea favoured the initial H. armigera larval incidence. Variogram analysis revealed moderate to strong larval aggregation (spatial dependence) of H. armigera in all nine sampling weeks. Based on the range value of the variogram, the average aggregation distance of H. armigera larvae in pigeonpea was estimated to be 2425.48 m. Voronoi diagrams illustrated the spatial heterogeneity of H. armigera larva between sampling weeks, which can be linked to availability of food sources.

CONCLUSION

This study witnessed intrapopulation variability in H. armigera larvae associated with geographical space and temporal patterns. Based on our findings, a sampling distance of 2425.48 m may be used in larger pigeonpea fields (experimental area) to reduce scouting fatigue. The interpolated maps generated in this study may be of value in developing effective H. armigera larva monitoring and management tools in pigeonpea crop.

Glomerular function in relation to fine airborne particulate matter in a representative population sample

From 1990 until 2017, global air-pollution related mortality increased by 40%. Few studies addressed the renal responses to ultrafine particulate [≤ 2.5 µm (PM2.5)], including black carbon (BC), which penetrate into the blood stream. In a Flemish population study, glomerular filtration estimated from serum creatinine (eGFR) and the urinary albumin-to-creatinine ratio were measured in 2005–2009 in 820 participants (women, 50.7%; age, 51.1 years) with follow-up of 523 after 4.7 years (median). Serum creatinine, eGFR, chronic kidney disease (eGFR < 60 mL/min/1.73 m²) and microalbuminuria (> 3.5/> 2.5 mg per mmol creatinine in women/men) were correlated in individual participants via their residential address with PM_2.5 [median 13.1 (range 0.3–2.9) μg/m³] and BC [1.1 (0.3–18) μg/m³], using mixed models accounting for address clusters. Cross-sectional and longitudinally, no renal outcome was associated with PM_2.5 or BC in models adjusted for sex and baseline or time varying covariables, including age, blood pressure, heart rate, body mass index, plasma glucose, the total-to-HDL serum cholesterol ratio, alcohol intake, smoking, physical activity, socioeconomic class, and antihypertensive treatment. The subject-level geocorrelations of eGFR change with to BC and PM2.5 were 0.13 and 0.02, respectively (P ≥ 0.68). In conclusion, in a population with moderate exposure, renal function was unrelated to ultrafine particulate.

Spatiotemporal analysis of traffic congestion, air pollution, and exposure vulnerability in Tanzania

Using new satellite data from the European Space Agency's Sentinel-5P system, this article investigates the spatial and temporal dynamics of vehicular traffic congestion, air pollution, and the distributional impacts on vulnerable populations in Dar es Salaam, Tanzania. The metro region's rapid growth in vehicle traffic exceeds road network capacity, generating congestion, transport delays, and air pollution from excess fuel use. Dangerously high pollution levels from tailpipe emissions put the health of vulnerable residents at risk, calling for the need to adopt continuous air-quality monitoring and effective pollution control. Our results highlight significant impacts of seasonal weather and wind-speed factors on the spatial distribution and intensity of air pollution from vehicle emissions, which vary widely by area. In seasons when weather factors maximize pollution, the worst exposure occurs along the wind path of high-traffic roadways. The study identifies priority areas for reducing congestion to yield the greatest exposure reduction for young children and the elderly in poor households. This new research direction, based only on the use of free global information sources with the same coverage for all cities, offers metropolitan areas in developing regions the opportunity to benefit from the rigorous analyses traditionally limited to well-endowed cites in developing countries.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

Reported Neighborhood Traffic and the Odds of Asthma/Asthma-Like Symptoms: A Cross-Sectional Analysis of a Multi-Racial Cohort of Children

Asthma in children poses a significant clinical and public health burden. We examined the association between reported neighborhood traffic (a proxy for traffic-related air pollution) and asthma among 855 multi-racial children aged 4-8 years old who participated in the Environmental Influences on Child Health Outcomes (ECHO) cohort. We hypothesized that high neighborhood traffic density would be associated with the prevalence of asthma. Asthma/asthma-like symptoms (defined as current and/or past physician diagnosed asthma, past wheezing, or nighttime cough or wheezing in the past 12 months) was assessed by parental report. The relationship between neighborhood traffic and asthma/asthma-like symptoms was assessed using logistic regression. The prevalence of asthma/asthma-like symptoms among study participants was 23%, and 15% had high neighborhood traffic. Children with significant neighborhood traffic had a higher odds of having asthma/asthma-like symptoms than children without neighborhood traffic [adjusted OR = 2.01 (95% CI: 1.12, 3.62)] after controlling for child's race-ethnicity, age, sex, maternal education, family history of asthma, play equipment in the home environment, public parks, obesity and prescribed asthma medication. Further characterization of neighborhood traffic is needed since many children live near high traffic zones and significant racial/ethnic disparities exist.

Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest

We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 μm (PM_2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.

Estimating seasonal variations of realistic exposure doses and risks to organs due to ambient particulate matter -bound metals of Delhi

This study aims to calculate deposition of PM_2.5 -bound hazardous metals in different organs after inhalation of particulate matter for the Delhi (India), and to estimate risks to organs following inhalation. Bio-accessible fractions of three PM-associated carcinogenic metals (As, Pb &Cd) were calculated using the metal values in simulated lung fluids. Depositions of metals in different organs were calculated using an integrated model consists of HRT and PBPK models. The calculation indicates that the major or significant deposition of metal Pb occurs in tissues, such as bone, muscle and blood. Most of the depositions of Cd happens in lung whereas most of the depositions of As happens in lung, muscle and skin. Most of the deposition of studied metals was found in lung (45% for arsenic and 70% for cadmium of their bio -dissolved contents). The following order of depositions of metals in different tissues were found (from highest deposition to smallest deposition): As: Lung > muscle = liver; Pb: bone > blood > muscle; Cd: lung > intestine. The combined exposures of PM_2.5 and its associated metals were found to give interaction-based hazard index greater than 1 for several months of the year, indicating a chance of health risk. Hazard quotient (HQ) <1 was seen for ingestion and dermal pathways, indicating no cause of concern. Findings indicate the need for doing periodic monitoring and estimating deposition doses and exposure risks of PM-associated metals to lungs and other organs for protecting human health.

Air pollution exposure and bladder, kidney and urinary tract cancer risk: A systematic review

BACKGROUND

Exposure to outdoor air pollution has been linked to lung cancer, and suspicion arose regarding bladder, kidney, and urinary tract cancer (urological cancers). However, most of evidence comes from occupational studies; therefore, little is known about the effect of exposure to air pollution on the risk of urological cancers in the general population.

METHOD

We systematically searched Medline, Scopus, and Web of Science for articles investigating the associations between long-term exposure to air pollution and the risk of urological cancer (incidence or mortality). We included articles using a specific air pollutant (PM₁₀, PM_2.5, …) or proxies (traffic, proximity index …). We assessed each study's quality with the Newcastle-Ottawa scale and rated the quality of the body of evidence for each pollutant-outcome with the GRADE approach. The different study methodologies regarding exposure or outcome prevented us to perform a meta-analysis.

RESULTS

twenty articles (four case-control, nine cohort, and seven ecologic) met our inclusion criteria and were included in this review: eighteen reported bladder, six kidney, and two urinary tract. Modeling air pollutants was the most common exposure assessment method. Most of the included studies reported positive associations between air pollution and urological cancer risk. However, only a few reached statistical significance (e.g. for bladder cancer mortality, adjusted odds-ratio of 1.13 (1.03-1.23) for an increase of 4.4 μg.m-3 of PM_2.5). Most studies inadequately addressed confounding, and cohort studies had an insufficient follow-up.

DISCUSSION

Overall, studies suggested positive (even though mostly non-significant) associations between air pollution exposure and bladder cancer mortality and kidney cancer incidence. We need more studies with better confounding control and longer follow-ups.

Harbor and Intra-City Drivers of Air Pollution: Findings from a Land Use Regression Model, Durban, South Africa

Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM₁₀ and PM_2.5), sulphur dioxide (SO₂), and nitrogen dioxide (NO₂) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m³ for NO₂, SO₂, PM₁₀, and PM_2.5, respectively). Furthermore, higher levels of NO₂ and SO₂ were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO₂ models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO₂ models were less robust with lower R² annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R² for PM₁₀ models ranged from 52% to 80% while for PM_2.5 models this range was 61–76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO₂ concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.

Probabilistic predictive principal component analysis for spatially misaligned and high-dimensional air pollution data with missing observations

Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM2.5), in which data is usually not measured at all study locations. PM2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lower-dimensional representative scores of such multi-pollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatial structures that can be well predicted at unmeasured locations. However, these approaches require complete data, whereas multi-pollutant data tends to have complex missing patterns in practice. We propose probabilistic versions of predictive PCA which allow for flexible model-based imputation that can account for spatial information and subsequently improve the overall predictive performance.

Prenatal exposure to traffic and ambient air pollution and infant weight and adiposity: The Healthy Start study

BACKGROUND

Prenatal exposures to ambient air pollution and traffic have been associated with adverse birth outcomes, and may also lead to an increased risk of obesity. Obesity risk may be reflected in changes in body composition in infancy.

OBJECTIVE

To estimate associations between prenatal ambient air pollution and traffic exposure, and infant weight and adiposity in a Colorado-based prospective cohort study.

METHODS

Participants were 1125 mother-infant pairs with term births. Birth weight was recorded from medical records and body composition measures (fat mass, fat-free mass, and adiposity [percent fat mass]) were evaluated via air displacement plethysmography at birth (n = 951) and at ~5 months (n = 574). Maternal residential address was used to calculate distance to nearest roadway, traffic density, and ambient concentrations of fine particulate matter (PM_2.5) and ozone (O₃) via inverse-distance weighted interpolation of stationary monitoring data, averaged by trimester and throughout pregnancy. Adjusted linear regression models estimated associations between exposures and infant weight and body composition.

RESULTS

Participants were urban residents and diverse in race/ethnicity and socioeconomic status. Average ambient air pollutant concentrations were generally low; the median, interquartile range (IQR), and range of third trimester concentrations were 7.3 μg/m³ (IQR: 1.3, range: 3.3-12.7) for PM_2.5 and 46.3 ppb (IQR: 18.4, range: 21.7-63.2) for 8-h maximum O_3. Overall there were few associations between traffic and air pollution exposures and infant outcomes. Third trimester O₃ was associated with greater adiposity at follow-up (2.2% per IQR, 95% CI 0.1, 4.3), and with greater rates of change in fat mass (1.8 g/day, 95% CI 0.5, 3.2) and adiposity (2.1%/100 days, 95% CI 0.4, 3.7) from birth to follow-up.

CONCLUSIONS

We found limited evidence of an association between prenatal traffic and ambient air pollution exposure and infant body composition. Suggestive associations between prenatal ozone exposure and early postnatal changes in body composition merit further investigation.

Spatial Assessment of Health Economic Losses from Exposure to Ambient Pollutants in China

Increasing emissions of ambient pollutants have caused considerable air pollution and negative health impact for human in various regions of China over the past decade. The resulting premature mortality and excessive morbidity caused huge human economic losses to the entire society. To identify the differences of health economic losses in various regions of China and help decision-making on targeting pollutants control, spatial assessment of health economic losses due to ambient pollutants in China is indispensable. In this study, to better represent the spatial variability, the satellite-based retrievals of the concentrations of various pollutants (PM10, PM2.5, O3, NO2, SO2 and CO) for the time period from 2007 to 2017 in China are used instead of using in-situ data. Population raster data were applied together with exposure-response function to calculate the spatial distribution of health impact and then the health impact is further quantified by using amended human capital (AHC) approach. The results which presented in a spatial resolution of 0.25° × 0.25°, show the signification contribution from the spatial assessment to revealing the spatial distribution and variance of health economic losses in various regions of China. Spatial assessment of overall health economic losses is different from conventional result due to more detail spatial information. This spatial assessment approach also provides an alternative method for losses measurement in other fields.

Assessment of Daily Personal PM2.5 Exposure Level According to Four Major Activities among Children

Particulate matters less than 2.5 micrometers in diameter (PM2.5), whose concentration has increased in Korea, has a considerable impact on health. From a risk management point of view, there has been interest in understanding the variations in real-time PM2.5 concentrations per activity in different microenvironments. We analyzed personal monitoring data collected from 15 children aged 6 to 11 years engaged in different activities such as commuting in a car, visiting a commercial building, attending an education institute, and resting inside home from October 2018 to March 2019. The fraction of daily mean exposure duration per activity was 72.7 ± 18.7% for resting inside home, 27.2 ± 14.4% for attending an education institute, and 11.5 ± 9.6% and 5.3 ± 5.9% for visiting a commercial building, commuting in a car, respectively. Daily median (interquartile range) PM2.5 exposure amount was 88.9 (55.9–159.7) μg in houses and that in education buildings was 43.3 (22.9–55.6) μg. Real-time PM2.5 exposure levels varied by person and time of day (p-value < 0.05). This study demonstrated that our real-time personal monitoring and data analysis methodologies were effective in detecting polluted microenvironments and provided a potential person-specific management strategy to reduce a person’s exposure level to PM2.5.

Self-reported traffic-related air pollution and respiratory symptoms among adults in an area with modest levels of traffic

Health effects of traffic-related air pollution (TRAP) concentrations in densely populated areas are previously described. However, there is still a lack of knowledge of the health effects of moderate TRAP levels. The aim of the current study, a population-based survey including 16 099 adults (response rate 33%), was to assess the relationship between TRAP estimates and respiratory symptoms in an area with modest levels of traffic; Telemark County, Norway. Respondents reported respiratory symptoms the past 12 months and two TRAP exposure estimates: amount of traffic outside their bedroom window and time spent by foot daily along a moderate to heavy traffic road. Females reported on average more symptoms than males. Significant relationships between traffic outside their bedroom window and number of symptoms were only found among females, with the strongest associations among female occasional smokers (incidence rate ratio [IRR], 1.75, 95% confidence interval (CI) [1.16–2.62] for moderate or heavy traffic compared to no traffic). Significant relationship between time spent daily by foot along a moderate to heavy traffic road and number of symptoms was found among male daily smokers (IRR 1.09, 95% CI [1.04–1.15] per hour increase). Associations between traffic outside bedroom window and each respiratory symptom were found. Significant associations were primarily detected among females, both among smokers and non-smokers. Significant associations between time spent by foot daily along a moderate to heavy traffic road (per hour) and nocturnal dyspnoea (odds ratio (OR) 1.20, 95% CI [1.05–1.38]), nocturnal chest tightness (OR 1.13 [1.00–1.28]) and wheezing (OR 1.14 [1.02–1.29]) were found among daily smokers, primarily men. Overall, we found significant associations between self-reported TRAP exposures and respiratory symptoms. Differences between genders and smoking status were identified. The findings indicate an association between TRAP and respiratory symptoms even in populations exposed to modest levels of TRAP.

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability. This study compared 16 algorithms to predict annual average fine particle (PM_2.5) and nitrogen dioxide (NO₂) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM_2.5 and 2399 sites for NO₂), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM_2.5) and 1396 sites (NO₂) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites. For PM_2.5, the models performed similarly across algorithms with a mean CV R² of 0.59 and a mean EV R² of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R²~0.63; EV R² 0.58-0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R² 0.48-0.57; EV R² 0.39-0.46). Most of the PM_2.5 model predictions at ESCAPE sites were highly correlated (R² > 0.85, with the exception of predictions from the artificial neural network). For NO₂, the models performed even more similarly across different algorithms, with CV R²s ranging from 0.57 to 0.62, and EV R²s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R² > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM_2.5 models whilst dispersion model estimates and traffic variables were most important for NO₂ models in all algorithms that allow assessment of the importance of variables. Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

Land Use Regression models for 60 volatile organic compounds: Comparing Google Point of Interest (POI) and city permit data

Land Use Regression (LUR) models of Volatile Organic Compounds (VOC) normally focus on land use (e.g., industrial area) or transportation facilities (e.g., roadway); here, we incorporate area sources (e.g., gas stations) from city permitting data and Google Point of Interest (POI) data to compare model performance. We used measurements from 50 community-based sampling locations (2013-2015) in Minneapolis, MN, USA to develop LUR models for 60 VOCs. We used three sets of independent variables: (1) base-case models with land use and transportation variables, (2) models that add area source variables from local business permit data, and (3) models that use Google POI data for area sources. The models with Google POI data performed best; for example, the total VOC (TVOC) model has better goodness-of-fit (adj-R²: 0.56; Root Mean Square Error [RMSE]: 0.32 μg/m³) as compared to the permit data model (0.42; 0.37) and the base-case model (0.26; 0.41). Area source variables were selected in over two thirds of models among the 60 VOCs at small-scale buffer sizes (e.g., 25 m-500 m). Our work suggests that VOC LUR models can be developed using community-based sampling and that models improve by including area sources as measured by business permit and Google POI data.

Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area

The association between particulate pollution and cardiovascular morbidity and mortality is well established. While the cardiovascular effects of nationally regulated criteria pollutants (e.g., fine particulate matter [PM_2.5] and nitrogen dioxide) have been well documented, there are fewer studies on particulate pollutants that are more specific for traffic, such as black carbon (BC) and particle number (PN). In this paper, we synthesized studies conducted in the Greater Boston Area on cardiovascular health effects of traffic exposure, specifically defined by BC or PN exposure or proximity to major roadways. Large cohort studies demonstrate that exposure to traffic-related particles adversely affect cardiac autonomic function, increase systemic cytokine-mediated inflammation and pro-thrombotic activity, and elevate the risk of hypertension and ischemic stroke. Key patterns emerged when directly comparing studies with overlapping exposure metrics and population cohorts. Most notably, cardiovascular risk estimates of PN and BC exposures were larger in magnitude or more often statistically significant compared to those of PM_2.5 exposures. Across multiple exposure metrics (e.g., short-term vs. long-term; observed vs. modeled) and different population cohorts (e.g., elderly, individuals with co-morbidities, young healthy individuals), there is compelling evidence that BC and PN represent traffic-related particles that are especially harmful to cardiovascular health. Further research is needed to validate these findings in other geographic locations, characterize exposure errors associated with using monitored and modeled traffic pollutant levels, and elucidate pathophysiological mechanisms underlying the cardiovascular effects of traffic-related particulate pollutants. Implications: Traffic emissions are an important source of particles harmful to cardiovascular health. Traffic-related particles, specifically BC and PN, adversely affect cardiac autonomic function, increase systemic inflammation and thrombotic activity, elevate BP, and increase the risk of ischemic stroke. There is evidence that BC and PN are associated with greater cardiovascular risk compared to PM_2.5. Further research is needed to elucidate other health effects of traffic-related particles and assess the feasibility of regulating BC and PN or their regional and local sources.

Estimation of health benefits from air quality improvement using the MODIS AOD dataset in Seoul, Korea

BACKGROUND

Exposure to fine particles in the atmosphere can adversely affect health and even lead to premature death. Recently, South Korea has attracted attention because of its rapid increase in the concentration of Particulate Matter (PM).

OBJECTIVES

We estimated the economic benefits of reducing PM₁₀ in Seoul, South Korea, based on MODerate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD). Based on the retrieved PM₁₀ data, we estimated its effects on overall health in each district of Seoul, Korea between 2014 and 2015.

METHODS

The relationships between MODIS AOD and ground-based PM₁₀ data were identified in different seasons in South Korea between 2012 and 2013 using the linear regression model. The health benefits were estimated by the Benefits Mapping and Analysis Program (Benmap) using the scenarios from the World Health Organization (WHO).

RESULTS

The correlation between MODIS AOD and PM₁₀ concentration differed with the season. There was a higher correlation between MODIS AOD and PM₁₀ concentration in winter (R = 0.57) than there was in other seasons. Based on the MODIS AOD, the average annual PM₁₀ concentration in Seoul was higher in 2014 than it was in 2015, at values of 45.7 μg/m³, and 41.6 μg/m³, respectively. The greatest economic benefit of reducing PM₁₀ concentration (WHO annual standard of 20 μg/m³) was in 2014. This benefit was estimated to be 7022 (95% CI: 599, 20496), 2617 (95% CI: 216, 7750), and 1328 (95% CI: -159, 4679) billion KRW for all-cause, cardiovascular, and respiratory mortalities in 2014 and 2015, respectively.

CONCLUSIONS

These results demonstrate that, despite considerable improvements in air quality in recent decades, there is still a need for countermeasures to prevent economic loss due to air pollution in Seoul.

High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO₂, NO_x, PM_2.5, PM_{2.5absorbance} and PM_10. The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.

Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China

Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired. This study took five highly populated and severely polluted provinces along the Huaihe River, China, as the research area. The ground-level concentrations of four major air pollutants including nitrogen dioxide (NO₂), sulfate dioxide (SO₂), particulate matters with diameter equal or less than 10 (PM₁₀) or 2.5 micron (PM_2.5) were estimated based on relevant remote sensing data using the geographically weighted regression (GWR) model. The health impacts of these pollutants were then assessed with the aid of co-located gridded population data. The results show that the annual average concentrations of ground-level NO₂, SO₂, PM₁₀, and PM_2.5 in 2016 were 31 µg/m³, 26 µg/m³, 100 µg/m³, and 59 µg/m³, respectively. In terms of the health impacts attributable to NO₂, SO₂, PM₁₀, and PM_2.5, there were 546, 1788, 10,595, and 8364 respiratory deaths, and 1221, 9666, 46,954, and 39,524 cardiovascular deaths, respectively. Northern Henan, west-central Shandong, southern Jiangsu, and Wuhan City in Hubei are prone to large health risks. Meanwhile, air pollutants have an overall greater impact on cardiovascular disease than respiratory disease, which is primarily attributable to the inhalable particle matters. Our findings provide a good reference to local decision makers for the implementation of further emission control strategies and possible health impacts assessment.

Childhood respiratory allergies and symptoms in highly polluted area of Central Europe

The study investigated the associations between the prevalence of the childhood respiratory diseases and the long-term exposure to air pollution in the burdened area of Moravian-Silesian Region in the Czech Republic. The health data were collected during 2014 in 7,239 children 5, 9, 13 and 17 years of age. Exposure to PM₁₀ and NO₂ in the residence addresses was based on dispersion models and GIS based traffic-related indicators. PM₁₀ levels were positively associated with both lifetime (OR 1.35; 95%CI 1.09-1.67) and current (OR 1.32; 95%CI 1.05-1.67) allergic rhinitis; current asthma was associated negatively. The associations between traffic indicator and respiratory health were not found. On the other hand, marked positive associations were found between the respiratory diseases and symptom severity structured into ordinal variables, and PM₁₀ and NO₂. Modelled long-term exposure to air pollution was associated with childhood allergic rhinitis and deterioration of the respiratory symptoms.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters

The Mexico City Metropolitan Area (MCMA) is one of the largest and most populated urban environments in the world and experiences high air pollution levels. To develop models that estimate pollutant concentrations at fine spatiotemporal scales and provide improved air pollution exposure assessments for health studies in Mexico City. We developed finer spatiotemporal land use regression (LUR) models for PM2.5, PM10, O3, NO2, CO and SO2 using mixed effect models with the Least Absolute Shrinkage and Selection Operator (LASSO). Hourly traffic density was included as a temporal variable besides meteorological and holiday variables. Models of hourly, daily, monthly, 6-monthly and annual averages were developed and evaluated using traditional and novel indices. The developed spatiotemporal LUR models yielded predicted concentrations with good spatial and temporal agreements with measured pollutant levels except for the hourly PM2.5, PM10 and SO2. Most of the LUR models met performance goals based on the standardized indices. LUR models with temporal scales greater than one hour were successfully developed using mixed effect models with LASSO and showed superior model performance compared to earlier LUR models, especially for time scales of a day or longer. The newly developed LUR models will be further refined with ongoing Mexico City air pollution sampling campaigns to improve personal exposure assessments.

Frequency of heavy vehicle traffic and association with DNA methylation at age 18 years in a subset of the Isle of Wight birth cohort

Assessment of changes in DNA methylation (DNA-m) has the potential to identify adverse environmental exposures. To examine DNA-m among a subset of participants (n = 369) in the Isle of Wight birth cohort who reported variable near resident traffic frequencies. We used self-reported frequencies of heavy vehicles passing by the homes of study subjects as a proxy measure for TRAP, which were: never, seldom, 10 per day, 1-9 per hour and >10 per hour. Methylation of cytosine-phosphate-guanine (CpG) dinucleotide sequences in the DNA was assessed from blood samples collected at age 18 years (n = 369) in the F1 generation. We conducted an epigenome wide association study to examine CpGs related to the frequency of heavy vehicles passing by subjects' homes, and employed multiple linear regression models to assess potential associations. We repeated some of these analysis in the F2 generation (n = 140). Thirty-five CpG sites were associated with heavy vehicular traffic. After adjusting for confounders, we found 23 CpGs that were more methylated, and 11 CpGs that were less methylated with increasing heavy vehicular traffic frequency among all subjects. In the F2 generation, 2 of 31 CpGs were associated with traffic frequencies and the direction of the effect was the same as in the F1 subset while differential methylation of 7 of 31 CpG sites correlated with gene expression. Our findings reveal differences in DNA-m in participants who reported higher heavy vehicular traffic frequencies when compared to participants who reported lower frequencies.

Lead in Air in Bangladesh: Exposure in a Rural Community with Elevated Blood Lead Concentrations among Young Children

Previous evaluations of a birth cohort in the Munshiganj District of Bangladesh had found that over 85% of 397 children aged 2⁻3 years had blood lead concentrations above the United States Centers for Disease Control and Prevention's reference level of 5 μg/dL. Studies in urban areas of Bangladesh have found elevated levels of lead in the air due to industries and remaining contamination from the historic use of leaded gasoline. Sources of lead in rural areas of Bangladesh remain unknown. We conducted air sampling in both residential and industrial sites in Munshiganj to determine whether children are exposed to elevated lead concentrations in the air and study the association between the children's blood lead levels and sampled air lead concentrations. Residential and industrial air samples in Munshiganj were found to have elevated lead concentrations (mean 1.22 μg/m³) but were not found to be associated with the observed blood lead concentrations. Lead in air is an important environmental health exposure risk to the for children in Munshiganj, and further research may shed light on specific sources to inform exposure prevention and mitigation programs.

Development of European NO2 Land Use Regression Model for present and future exposure assessment: Implications for policy analysis

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO₂ concentrations. The model was built using NO₂ concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO₂ concentrations, like levels of activity intensity and NO_x emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R² = 0.53). Output predictions of annual average concentrations of NO₂ were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.

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

Road traffic induces air and noise pollution in urban environments having negative impacts on human health. Thus, estimating exposure to road traffic air and noise pollution (hereafter, air and noise pollution) is important in order to improve the understanding of human health outcomes in epidemiological studies. The aims of this review are (i) to summarize current practices of modelling and exposure assessment techniques for road traffic air and noise pollution (ii) to highlight the potential of existing tools and techniques for their combined exposure assessment for air and noise together with associated challenges, research gaps and priorities. The study reviews literature about air and noise pollution from urban road traffic, including other relevant characteristics such as the employed dispersion models, Geographic Information System (GIS)-based tool, spatial scale of exposure assessment, study location, sample size, type of traffic data and building geometry information. Deterministic modelling is the most frequently used assessment technique for both air and noise pollution of short-term and long-term exposure. We observed a larger variety among air pollution models as compared to the applied noise models. Correlations between air and noise pollution vary significantly (0.05-0.74) and are affected by several parameters such as traffic attributes, building attributes and meteorology etc. Buildings act as screens for the dispersion of pollution, but the reduction effect is much larger for noise than for air pollution. While, meteorology has a greater influence on air pollution levels as compared to noise, although also important for noise pollution. There is a significant potential for developing a standard tool to assess combined exposure of traffic related air and noise pollution to facilitate health related studies. GIS, due to its geographic nature, is well established and has a significant capability to simultaneously address both exposures.

Comparison of land use regression models for NO2 based on routine and campaign monitoring data from an urban area of Japan

BACKGROUND

Typically, land use regression (LUR) models have been developed using campaign monitoring data rather than routine monitoring data. However, the latter have advantages such as low cost and long-term coverage. Based on the idea that LUR models representing regional differences in air pollution and regional road structures are optimal, the objective of this study was to evaluate the validity of LUR models for nitrogen dioxide (NO₂) based on routine and campaign monitoring data obtained from an urban area.

METHOD

We selected the city of Suita in Osaka (Japan). We built a model based on routine monitoring data obtained from all sites (routine-LUR-All), and a model based on campaign monitoring data (campaign-LUR) within the city. Models based on routine monitoring data obtained from background sites (routine-LUR-BS) and based on data obtained from roadside sites (routine-LUR-RS) were also built. The routine LUR models were based on monitoring networks across two prefectures (i.e., Osaka and Hyogo prefectures). We calculated the predictability of the each model. We then compared the predicted NO₂ concentrations from each model with measured annual average NO₂ concentrations from evaluation sites.

RESULTS

The routine-LUR-All and routine-LUR-BS models both predicted NO₂ concentrations well: adjusted R²=0.68 and 0.76, respectively, and root mean square error=3.4 and 2.1ppb, respectively. The predictions from the routine-LUR-All model were highly correlated with the measured NO₂ concentrations at evaluation sites. Although the predicted NO₂ concentrations from each model were correlated, the LUR models based on routine networks, and particularly those based on all monitoring sites, provided better visual representations of the local road conditions in the city.

CONCLUSION

The present study demonstrated that LUR models based on routine data could estimate local traffic-related air pollution in an urban area. The importance and usefulness of data from routine monitoring networks should be acknowledged.

Evaluation of DNA damage in traffic police wardens of Pakistan due to cadmium and zinc

Air quality in urban areas is generally poor especially at traffic intersections and roadsides due to continuous vehicular emissions comprising poly aromatic hydrocarbons, heavy metals, benzene, diesel soot etc. The objective of this study was to compare the primary DNA damage in traffic police wardens occupationally exposed to airborne Cd and Zn (exposed group) and educational institution with negligible exposure to airborne Cd and Zn (control group). Blood levels of Cd and Zn in traffic police wardens and control group were determined by Flame Atomic Absorption Spectroscopy (FAAS) and DNA damage was assessed by using Comet assay. The results of this study showed significantly higher amount of Cd (0.18±0.06mgL^-1) and Zn (4.87±1.34mgL^-1) in the blood of traffic police wardens as compared to control group and according to World Health Organization, these values are 18 and 3 times more to the permissible limit of Cd and Zn respectively in human blood. In addition, significantly higher numbers of DNA damaged cells (28±13%) were observed in traffic police wardens as compared to control group (3.6±2%). Comet tail length was found to be doubled (4.7±1.7μm) in traffic police wardens as compared to the control group (2±1.2μm). These results could be linked to the concentrations of Cd and Zn in blood of traffic police wardens. In conclusion, our results showed that accumulation of Cd and Zn was higher in traffic police wardens due to air pollution (Zn and Cd) and has more damaged DNA of traffic police wardens in Pakistan.

Ambient PM2.5 air pollution exposure and hepatocellular carcinoma incidence in the United States

PURPOSE

To conduct the first epidemiologic study prospectively examining the association between particulate matter air pollution < 2.5 µm in diameter (PM2.5) exposure and hepatocellular carcinoma (HCC) risk in the U.S.

METHODS

Surveillance, Epidemiology, and End Results (SEER) provided information on HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries across the U.S. Ambient PM2.5 exposure was estimated by linking the SEER county with a spatial PM2.5 model using a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios and 95% confidence intervals (CIs) for the association between ambient PM2.5 exposure per 10 µg/m3 increase and HCC risk adjusting for individual-level age at diagnosis, sex, race, year of diagnosis, SEER registry, and county-level information on health conditions, lifestyle, demographic, socioeconomic, and environmental factors.

RESULTS

Higher levels of ambient PM2.5 exposure were associated with a statistically significant increased risk for HCC (n = 56,245 cases; adjusted IRR per 10 µg/m3 increase = 1.26, 95% CI 1.08, 1.47; p < 0.01).

CONCLUSIONS

If confirmed in studies with individual-level PM2.5 exposure and risk factor information, these results suggest that ambient PM2.5 exposure may be a risk factor for HCC in the U.S.

School environmental conditions and links to academic performance and absenteeism in urban, mid-Atlantic public schools

School facility conditions, environment, and perceptions of safety and learning have been investigated for their impact on child development. However, it is important to consider how the environment separately influences academic performance and attendance after controlling for school and community factors. Using results from the Maryland School Assessment, we considered outcomes of school-level proficiency in reading and math plus attendance and chronic absences, defined as missing 20 or more days, for grades 3-5 and 6-8 at 158 urban schools. Characteristics of the environment included school facility conditions, density of nearby roads, and an index industrial air pollution. Perceptions of school safety, learning, and institutional environment were acquired from a School Climate Survey. Also considered were neighborhood factors at the community statistical area, including demographics, crime, and poverty based on school location. Poisson regression adjusted for over-dispersion was used to model academic achievement and multiple linear models were used for attendance. Each 10-unit change in facility condition index, denoting worse quality buildings, was associated with a decrease in reading (1.0% (95% CI: 0.1-1.9%) and math scores (0.21% (95% CI: 0.20-0.40), while chronic absences increased by 0.75% (95% CI: 0.30-1.39). Each log increase the EPA's Risk Screening Environmental Indicator (RSEI) value for industrial hazards, resulted in a marginally significant trend of increasing absenteeism (p < 0.06), but no association was observed with academic achievement. All results were robust to school-level measures of racial composition, free and reduced meals eligibility, and community poverty and crime. These findings provide empirical evidence for the importance of the community and school environment, including building conditions and neighborhood toxic substance risk, on academic achievement and attendance.

Developing a Clinical Approach to Air Pollution and Cardiovascular Health

Nearly 3 billion people are exposed to household air pollution emitted from inefficient cooking and heating stoves, and almost the entire global population is exposed to detectable levels of outdoor air pollution from traffic, industry, and other sources. Over 3 million people die annually of ischemic heart disease or stroke attributed to air pollution, more than from traditional cardiac risk factors such as obesity, diabetes mellitus, or smoking. Clinicians have a role to play in reducing the burden of pollution-attributable cardiovascular disease. However, there currently exists no clear clinical approach to this problem. Here, we provide a blueprint for an evidence-based clinical approach to assessing and mitigating cardiovascular risk from exposure to air pollution. We begin with a discussion of the global burden of pollution-attributable cardiovascular disease, including a review of the mechanisms by which particulate matter air pollution leads to cardiovascular outcomes. Next, we offer a simple patient-screening tool using known risk factors for pollution exposure. We then discuss approaches to quantifying air pollution exposures and cardiovascular risk, including the development of risk maps for clinical catchment areas. We review a collection of interventions for household and outdoor air pollution, which clinicians can tailor to patients and populations at risk. Finally, we identify future research needed to quantify pollution exposures and validate clinical interventions. Overall, we demonstrate that clinicians can be empowered to mitigate the global burden of cardiovascular disease attributable to air pollution.