GIS-based modeling of traffic-related air pollution and public health risks: a systematic review with an emphasis on developing urban contexts

In the State of Qatar, recent statistics show a continued increase in the motor-vehicle fleet commensurate with population growth and economic development. This trend, together with the rapid expansion of urban areas and the increased dependence on automobiles, has resulted in an increase in pollution near traffic sources, indicating that the risk of exposure to vehicles’ emissions is higher and that these emissions must be considered in terms of their spatial and temporal occurrence. So far, there are no studies conducted in Qatar to evaluate the traffic-related air pollution. This study is carried out to monitor the levels of nitrogen dioxide (NO2) as a marker of pollution related to traffic. The aim of this study is to build a baseline for traffic-related pollution in Qatar by monitoring and modelling NO2 emissions. The levels of traffic-related air pollution by NO2 were investigated at six major intersections along the C-ring road. The relationship of NO2 was established with traffic volume in each intersection during December 2012 and March–April 2013. Significant differences were established between the pollutant concentrations in each intersection. The CALifornia LINE Source Dispersion Model, version 4 air dispersion model employed had estimated the influence of the measured NO2 concentration on the predicted NO2 values by 31.12%. The low percentage may have accounted for the uncertainties brought by the vehicle emission factor and non-availability of temporal dynamics during the time of sampling. Non-parametric Spearman’s correlation test resulted in a significant correlation between measured and predicted values of NO2 concentrations, rs = 0.525 and p = 0.012.

Individuals’ risk for cardiovascular disease is shaped by lifestyle factors such as participation in physical activity. Some studies have suggested that rates of physical activity may be higher in walkable neighborhoods that are more supportive of engaging in physical activity in daily life. However, walkable neighborhoods may also contain increased levels of traffic-related air pollution (TRAP). Traffic-related air pollution, often measured through a surrogate marker (e.g. NO2), has been associated cardiovascular disease risk and risk factors [1–4]. The higher levels of TRAP in walkable neighborhoods may in turn increase the likelihood of developing conditions like hypertension and diabetes. Our recent work assessed how walkability and TRAP jointly affect the odds of diabetes and hypertension in a sample of community-dwelling adults from Southern Ontario, Canada [5]. This article contains additional data on the probability and odds of hypertension and diabetes according to their walkability and TRAP exposures. Data on cardiovascular risk factors were collected using health administrative databases and environmental exposures were assessed using national land use regression models predicting ground level concentrations of NO2 and validated walkability indices. The included data were generated using logistic regression accounting for exposures, covariates, and neighborhood clustering. These data may be used as primary data in future health risk assessments and systematic reviews, or to aid in the design of studies examining interactions between built environment and TRAP exposures (e.g. sample size calculations).

Air pollution and human fertility rates

BackgroundPre-eclampsia is a common complication of pregnancy and is a major cause of fetal–maternal mortality and morbidity. Despite a number of plausible mechanisms by which air pollutants might contribute to...

Traffic-related air pollution (TRAP) remains a concern for public health. However, the exact mechanisms through which TRAP affects the respiratory system are still not fully understood. This study aimed to investigate the nasal microbiome change in healthy adults after short-term exposure to TRAP, contributing to the understanding of the adverse health effects associated with TRAP. A randomized crossover controlled trial was conducted from 9 March to 30 March 2024 among college students aged 19-24 years. Twenty healthy students were recruited through a baseline questionnaire survey and randomly assigned into two groups. One group followed a crowed-testing procedure: the park portion, a three-week washout period, and then the road portion, while the other group experienced the opposite procedure. Both groups were fully exposed to either a park environment or a road environment with high traffic volume. Nasal mucus samples were collected from the participants at the end of the trial, and then 16SrRNA sequencing was performed to analyze the differences in compositional structure and diversity of the nasal microbiome when volunteers were exposed to different levels of TRAP. The α-diversity indices, including the Chao1 index (p = 0.0097), observed species index (p = 0.0089), and Faith's PD index (p = 0.0255), demonstrated a significant increase in the nasal microbiome of healthy adults following short-term exposure to TRAP. Visualization through a two-dimensional NMDS plot (stress value < 0.2) indicated that nasal bacterial species distribution became richer after TRAP exposure. Furthermore, the relative abundance of nasal Firmicutes (Bacillota), Bacteroidota, and Actinobacteriota phyla, especially Firmicutes phylum, exhibited a richer distribution after conducting the trial in the road environment with high levels of TRAP, which was shown in the significance test of signature species. Collectively, our study indicates that short-term exposure to TRAP can affect the composition of the nasal microbiota in healthy adults. These findings offer a scientific basis for understanding how TRAP causes respiratory diseases.

Background: Current levels of traffic-related air pollution (TRAP) are associated with the development of childhood asthma, although some inconsistencies and heterogeneity remain. An important part of the uncertainty in studies of TRAP-associated asthma originates from uncertainties in the TRAP exposure assessment and assignment methods. In this work, we aim to systematically review the exposure assessment methods used in the epidemiology of TRAP and childhood asthma, highlight recent advances, remaining research gaps and make suggestions for further research. Methods: We systematically reviewed epidemiological studies published up until 8 September 2016 and available in Embase, Ovid MEDLINE (R), and “Transport database”. We included studies which examined the association between children’s exposure to TRAP metrics and their risk of “asthma” incidence or lifetime prevalence, from birth to the age of 18 years old. Results: We found 42 studies which examined the associations between TRAP and subsequent childhood asthma incidence or lifetime prevalence, published since 1999. Land-use regression modelling was the most commonly used method and nitrogen dioxide (NO2) was the most commonly used pollutant in the exposure assessments. Most studies estimated TRAP exposure at the residential address and only a few considered the participants’ mobility. TRAP exposure was mostly assessed at the birth year and only a few studies considered different and/or multiple exposure time windows. We recommend that further work is needed including e.g., the use of new exposure metrics such as the composition of particulate matter, oxidative potential and ultra-fine particles, improved modelling e.g., by combining different exposure assessment models, including mobility of the participants, and systematically investigating different exposure time windows. Conclusions: Although our previous meta-analysis found statistically significant associations for various TRAP exposures and subsequent childhood asthma, further refinement of the exposure assessment may improve the risk estimates, and shed light on critical exposure time windows, putative agents, underlying mechanisms and drivers of heterogeneity.

Introduction Studies suggest living in a more walkable neighborhood may protect against cardiovascular disease risk factors such as hypertension (HTN) and diabetes mellitus (DM) by encouraging physical activity. Walkable neighborhoods, however, often carry higher levels of traffic-related air pollution. Little is known regarding whether synergistic effects may exist between walkability and air pollution on these risk factors.Hypothesis We hypothesized that the association between traffic-related air pollution, hypertension, and diabetes mellitus would be stronger in more walkable areas.Methods We drew a cross-sectional sample of individuals ages 40-74 on January 1, 2008 from the CANHEART cohort. HTN and DM were ascertained using validated algorithms. Walkability (quintiles, Q5 highest, Q1 lowest) was measured using a validated index which has previously been shown to be inversely associated with obesity and diabetes. Exposure to nitrogen dioxide, a valid marker for traffic-related air pollution, was assessed using a land use regression models. The associations were tested using logistic regression with cluster-robust standard errors, adjusting for age, sex, area-level income, ethnicity, and comorbidities.Results 2,618,584 individuals were included in the analysis. Walkability was inversely associated with odds for HTN (Q5 vs. Q1 OR &amp;#61; 0.80, 95% CI: 0.79, 0.82) and DM (Q5 vs. Q1 OR &amp;#61; 0.89, 95% CI: 0.87, 0.91), while NO2 was positively associated with each (HTN: OR &amp;#61; 1.02 per 10 ppb (1.01, 1.03); DM: OR &amp;#61; 1.11 per 10 ppb (1.09, 1.13)). We observed significant interactions between walkability and NO2 on odds for HTN and DM, with stronger NO2 associations in the most walkable neighborhoods.Conclusions We observed significant interactions between traffic-related air pollution and walkability on odds for HTN and DM. This finding suggests that benefits from living in more walkable neighborhoods may be partially offset by stronger negative associations with air pollution.

Introduction: Studies suggest living in a more walkable neighborhood may protect against cardiovascular disease risk factors such as hypertension (HTN) and diabetes mellitus (DM) by encouraging physical activity. Walkable neighborhoods, however, often carry higher levels of traffic-related air pollution. Little is known regarding whether synergistic effects may exist between walkability and air pollution on these risk factors. Hypothesis: We hypothesized that the association between traffic-related air pollution, hypertension, and diabetes mellitus would be stronger in more walkable areas. Methods: We drew a cross-sectional sample of individuals ages 40-74 on January 1, 2008 from the CANHEART cohort. HTN and DM were ascertained using validated algorithms. Walkability (quintiles, Q5 highest, Q1 lowest) was measured using a validated index which has previously been shown to be inversely associated with obesity and diabetes. Exposure to nitrogen dioxide, a valid marker for traffic-related air pollution, was assessed using a land use regression models. The associations were tested using logistic regression with cluster-robust standard errors, adjusting for age, sex, area-level income, ethnicity, and comorbidities. Results: In total, 2,618,584 individuals were included in the analysis (mean (SD) age = 53.2 (9.2), 52% female). Walkability was inversely associated with odds for HTN (Q5 vs. Q1 OR = 0.80, 95% CI: 0.79, 0.82) and DM (Q5 vs. Q1 OR = 0.89, 95% CI: 0.87, 0.91), while NO 2 was positively associated with each (HTN: OR = 1.02 per 10 ppb (1.01, 1.03); DM: OR = 1.11 per 10 ppb (1.09, 1.13)). We observed significant interactions between walkability and NO 2 on odds for HTN and DM, with stronger NO 2 associations in the most walkable neighborhoods (Fig. 1). Conclusions: We observed significant interactions between traffic-related air pollution and walkability on odds for HTN and DM. This finding suggests that benefits from living in more walkable neighborhoods may be offset by stronger negative associations with air pollution.

This study compares land use regression (LUR) and geographically weighted regression (GWR) models in PM2.5 concentration mapping over California (USA). Results show that R2 values of LUR model are 0.78, 0.74 and 0.74 times lower than those of GWR model at annual, seasonal and monthly scales. Relative errors are 2.18, 1.79 and 1.60 times higher, and root mean square error (RMSE) are 1.48, 1.32 and 1.28 higher. Furthermore, performance difference is significant under polluted conditions, but is minor under clean conditions. It demonstrates that LUR model is effective under low concentration at short time scales.

Air pollution is associated with premature mortality and a wide spectrum of diseases. Traffic-related air pollution (TRAP) is one of the most concerning sources of air pollution for human exposure and health. Until TRAP levels can be significantly reduced on a global scale, there is a need for effective shorter-term strategies to prevent the adverse health effects of TRAP. A growing number of studies suggest that increasing antioxidant intake, through diet or supplementation, may reduce this burden of disease. In this paper, we conducted a non-systematic literature review to assess the available evidence on antioxidant-rich diets and antioxidant supplements as a strategy to mitigate adverse health effects of TRAP in human subjects. We identified 11 studies that fit our inclusion criteria; 3 of which investigated antioxidant-rich diets and 8 of which investigated antioxidant supplements. Overall, we found consistent evidence that dietary intake of antioxidants from adherence to the Mediterranean diet and increased fruit and vegetable consumption is effective in mitigating adverse health effects associated with TRAP. In contrast, antioxidant supplements, including fish oil, olive oil, and vitamin C and E supplements, presented conflicting evidence. Further research is needed to determine why antioxidant supplementation has limited efficacy and whether this relates to effective dose, supplement formulation, timing of administration, or population being studied. There is also a need to better ascertain if susceptible populations, such as children, the elderly, asthmatics and occupational workers consistently exposed to TRAP, should be recommended to increase their antioxidant intake to reduce their burden of disease. Policymakers should consider increasing populations’ antioxidant intake, through antioxidant-rich diets, as a relatively cheap and easy preventive measure to lower the burden of disease associated with TRAP.

How traffic-related air pollution generation and exposure is distributed among different population groups is an important environmental justice concern. From a social equity perspective, many questions arise at the metropolitan scale. Do socially disadvantaged communities have higher exposure levels to traffic-related air pollution? Do discrepancies exist wherein neighborhoods are not exposed to levels of pollution similar to those they themselves generate? And, is there a relationship between this discrepancy and social disadvantage? These questions are examined for the Montreal Metropolitan Region through the development of an integrated transport and emissions model. Two measures of traffic-related air pollution are estimated at the traffic analysis zone level: (1) generation (average emissions per household), and (2) exposure (average residential zone concentration). A social disadvantage index is also calculated that incorporates elements of social and material deprivation. Three levels of inequity exist regarding emissions, exposure, and socioeconomics. Social disadvantage was found to have a positive relationship with exposure, meaning that the most socially disadvantaged communities tend to experience the highest levels of traffic-related air pollution. Spatial discrepancies in emission generation versus emission exposure are also present for most of the metropolitan region. Furthermore, the communities that face a double burden of greater disadvantage and higher exposure also tend to create the lowest quantities of pollution.

Understanding the relationship between the spatial distribution of precipitation and crop yields on large scales (i.e., county, state, regional) while accounting for the spatial non-stationarity can help managers to better evaluate the long-term trends in agricultural productivity to make better assessments in food security, policy decisions, resource assessments, land and water resources enhancement, and management decisions. A relatively new technique, geographically weighted regression (GWR), has the ability to account for spatial non-stationarity with space. While its application is growing in other scientific disciplines (i.e., social sciences), the application of this new technique in agricultural settings has not been practiced. The geographic information system (GIS), along with two different statistical techniques [GWR and conventional ordinary least square regression (OLS)], was utilized to analyze the relationships between various precipitation categories and irrigated and rainfed maize and soybean yields for all 93 counties in Nebraska from 1996 to 2008. Precipitation was spatially interpolated in ArcGIS using a spline interpolation technique with zonal statistics. Both measured and GWR- and OLS-predicted yields were correlated to spatially interpolated annual (January 1 to December 31), seasonal (May 1 to September 30), and monthly (May, June, July, August, and September) precipitation for each county. Statewide average annual precipitation in Nebraska from 1996 to 2008 was 564 mm, with a maximum of 762 mm and minimum of 300 mm. Mean precipitation decreased gradually from May to September during the growing season. County average yields followed the same temporal trends as precipitation. When the OLS regression model was used, there was a general trend of linear correlation between observed yield and long-term average mean annual total precipitation with a varying coefficient of determination (R2). For rainfed crops, 67% of the variability in mean yield was explained by the mean annual precipitation. About 23% and 17% of the variability in mean yield was explained by mean annual precipitation for irrigated maize and soybean, respectively. However, the performance of the GWR technique in predicting the yields from spatially interpolated precipitation for irrigated and rainfed maize and soybean was significantly better than the performance of the OLS model. For both rainfed maize and soybean, 77% to 80% of the variation in yield was explained by the mean annual precipitation alone. For irrigated crops, 42% of the variation in the yield was explained by the mean annual precipitation. For rainfed crops, there was a strong correlation between seasonal precipitation and yield, with R2 values of 0.73 and 0.76 for maize and soybean, respectively. The mean annual total precipitation was a better predictor of rainfed maize yield than rainfed soybean yield. On a statewide average, July precipitation as a predictor had the greatest correlation with yields of both maize and soybean. June, July, and August precipitation had greater impact on maize yield than on soybean under rainfed conditions due to more sensitivity of maize to water stress than soybean. For irrigated yields, July precipitation had more impact on soybean yield than on maize. The performance of the GWR technique was superior to the OLS model in analyzing the relationship between yield and precipitation. The superiority of the GWR technique to OLS is mainly due to its ability to account for the impact of spatial non-stationarity on the precipitation vs. yield relationships.

Maternal serum metabolome and traffic-related air pollution exposure in pregnancy

Child serum metabolome and traffic-related air pollution exposure in pregnancy

For 1,612 elderly Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) followed for up to 10 years, we estimated residential-based local traffic-related exposures relying on the California Line Source Dispersion Model version 4 (CALINE4) for nitrogen oxides (NOx) and the SoundPLAN software package (Version 8.0; NAVCON, Fullerton, CA) that implements the Federal Highway Administration Traffic Noise Model (TNM) for noise, respectively. We used Cox proportional hazard models to estimate the joint effects of NOx or noise exposures and obesity, hyperglycemia, or low high-density lipoprotein (HDL) cholesterol. The risk of developing dementia/CIND among participants with hyperglycemia who also were exposed to high levels of NOx (≥3.44 parts per billion [ppb] [75th percentile]) or noise (≥65 dB) was 2.4 (1.4, 4.0) and 2.2 (1.7, 3.9), respectively. For participants with low HDL-cholesterol, the estimated hazard ratios for dementia/CIND were 2.5 (1.4, 4.3) and 1.8 (1.0, 3.0) for those also exposed to high levels of NOx (≥3.44 ppb) or noise (≥65 dB), respectively, compared with those without metabolic dysfunction exposed to low traffic-related air pollution or noise levels. Exposure to traffic-related air pollution or noise most strongly increases the risk of dementia/CIND among older Mexican-Americans living in California who also exhibit hyperglycemia or low HDL-cholesterol.