Association of outdoor air and noise pollution with unprovoked seizures and new onset epilepsy: A systematic review and meta-analysis.

Research has indicated that certain environmental exposures may increase the risk of unprovoked seizures and new onset epilepsy. This study aimed to synthesize the literature that has estimated the associations between short- and long-term exposure to outdoor air and noise pollution and the risk of unprovoked seizures and new onset epilepsy. We searched Embase, MEDLINE, Scopus, Web of Science, BIOSIS Previews, Latin American and Caribbean Health Sciences Literature, Proquest Dissertations and Theses, conference abstracts, and the gray literature and conducted citation tracing in June 2023. Observational and ecological studies assessing the associations of air and noise pollution with unprovoked seizures or new onset epilepsy were eligible. One reviewer extracted summary data. Using fixed and random effects models, we calculated the pooled risk ratios (RRs) for the studies assessing the associations between short-term exposure to air pollution and unprovoked seizures. Seventeen studies were included, 16 assessing the association of air pollution with seizures and one with epilepsy. Eight studies were pooled quantitatively. Ozone (O3; RR = .99, 95% confidence interval [CI] = .99-.99) and nitrogen dioxide (NO2) exposure adjusted for particulate matter (RR = 1.02, 95% CI = 1.01-1.02) on the same day, and carbon monoxide (CO) exposure 2 days prior (RR = 1.12, 95% CI = 1.02-1.22), were associated with seizure risk. A single study of air pollution and epilepsy did not report a significant association. The risk of bias and heterogeneity across studies was moderate or high. Short-term exposure to O3, NO2, and CO may affect the risk of seizures; however, the effect estimates for O3 and NO2 were minimal. Additional research should continue to explore these and the associations between outdoor air pollution and epilepsy and between noise pollution and seizures and epilepsy.

Tree characteristics and environmental noise in complex urban settings - A case study from Montreal, Canada.

Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate the spatial variation of urban tree canopy characteristics, indices of vegetation abundance, and environmental noise levels. Using Light Detection and Ranging point cloud data for 2015, we extracted the characteristics of 1,272,069 public and private trees across the island of Montreal, Canada. We distinguished needle-leaf from broadleaf trees, and calculated the percentage of broadleaf trees, the total area of the crown footprint, the mean crown centroid height, and the mean volume of crowns of trees that were located within 100m, 250m, 500m, and 1000m buffers around 87 in situ noise measurement sites. A random forest model incorporating tree characteristics, the normalized difference vegetation index (NDVI) values, and the distances to major urban noise sources (highways, railways and roads) was employed to estimate variation in noise among measurement locations. We found decreasing trends in noise levels with increases in total area of the crown footprint and mean crown centroid height. The percentages of increased mean squared error of the regression models indicated that in 500m buffers the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) had a stronger influence than NDVI (3.2%) in modeling noise levels; similar patterns of influence were observed using other buffers. Our findings suggest that municipal initiatives designed to reduce urban noise should account for tree features, and not just the number of trees or the overall amount of vegetation.

Integrating random forests and propagation models for high-resolution noise mapping.

The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g., vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e., mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was -0.03 dB(A), 2.67 dB(A), and 3.36 dB(A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available.

Exposure to Road Traffic Noise and Incidence of Acute Myocardial Infarction and Congestive Heart Failure: A Population-Based Cohort Study in Toronto, Canada.

BACKGROUND: Epidemiological evidence for the association between traffic-related noise and the incidence of major cardiovascular events such as acute myocardial infarction (AMI) and congestive heart failure (CHF) is inconclusive, especially in North America. OBJECTIVES: We evaluated the associations between long-term exposure to road traffic noise and the incidence of AMI and CHF. METHODS: Our study population comprised ∼1 million people 30-100 years of age who lived in Toronto, Canada, from 2001 to 2015 and were free of AMI (referred to as the AMI cohort) or CHF (the CHF cohort) at baseline. Outcomes were ascertained from health administrative databases using validated algorithms. Annual average noise levels were estimated as the A-weighted equivalent sound pressure level over the 24-h period (LAeq24) and during nighttime (LAeqNight), respectively, using propagation modeling, and assigned to participants' annual six-digit postal code addresses during follow-up. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for incident AMI and CHF in relation to LAeq24 and LAeqNight using random-effects Cox proportional hazards models adjusting for individual- and census tract-level covariates, including traffic-related air pollutants [e.g., ultrafine particles (UFPs) and nitrogen dioxide]. RESULTS: During follow-up, there were 37,441 AMI incident cases and 95,138 CHF incident cases. Each interquartile range change in LAeq24 was associated with an increased risk of incident AMI (HR=1.07; 95% CI: 1.06, 1.08) and CHF (HR=1.07; 95% CI: 1.06, 1.09). Similarly, LAeqNight was associated with incident AMI (HR=1.07; 95% CI: 1.05, 1.08) and CHF (HR=1.06; 95% CI: 1.05, 1.07). These results were robust to various sensitivity analyses and remained elevated after controlling for long-term exposure to UFPs and nitrogen dioxide. We found near-linear relationships between noise and the incidence of AMI and CHF with no evidence of threshold values. CONCLUSION: In this large cohort study in Toronto, Canada, chronic exposure to road traffic noise was associated with elevated risks for AMI and CHF incidence. https://doi.org/10.1289/EHP5809.

Traffic-Related Air Pollution and Carotid Plaque Burden in a Canadian City With Low-Level Ambient Pollution.

Background The association between fine particulate matter and cardiovascular disease has been convincingly demonstrated. The role of traffic-related air pollutants is less clear. To better understand the role of traffic-related air pollutants in cardiovascular disease development, we examined associations between NO2, carotid atherosclerotic plaque, and cardiometabolic disorders associated with cardiovascular disease. Methods and Results Cross-sectional analyses were conducted among 2227 patients (62.9±13.8 years; 49.5% women) from the Stroke Prevention and Atherosclerosis Research Centre (SPARC) in London, Ontario, Canada. Total carotid plaque area measured by ultrasound, cardiometabolic disorders, and residential locations were provided by SPARC medical records. Long-term outdoor residential NO2 concentrations were generated by a land use regression model. Associations between NO2, total carotid plaque area, and cardiometabolic disorders were examined using multiple regression models adjusted for age, sex, smoking, and socioeconomic status. Mean NO2 was 5.4±1.6 ppb in London, Ontario. NO2 was associated with a significant increase in plaque (3.4 mm2 total carotid plaque area per 1 ppb NO2), exhibiting a linear dose-response. NO2 was also positively associated with triglycerides, total cholesterol, and the ratio of low- to high-density lipoprotein cholesterol (P<0.05). Diabetes mellitus mediated the relationship between NO2 and total carotid plaque area (P<0.05). Conclusions Our results demonstrate that even low levels of traffic-related air pollutants are linked to atherosclerotic plaque burden, an association that may be partially attributable to pollution-induced diabetes mellitus. Our findings suggest that reducing ambient concentrations in cities with NO2 below current standards would result in additional health benefits. Given the billions of people exposed to traffic emissions, our study supports the global public health significance of reducing air pollution.

Association Between Road Traffic Noise and Incidence of Diabetes Mellitus and Hypertension in Toronto, Canada: A Population-Based Cohort Study.

Background Exposure to road traffic noise has been linked to cardiometabolic complications, such as elevated blood pressure and glucose dysregulation. However, epidemiologic evidence linking road traffic noise to diabetes mellitus and hypertension remains scarce. We examined associations between road traffic noise and the incidence of diabetes mellitus and hypertension in Toronto, Canada. Methods and Results Using the Ontario Population Health and Environment Cohort, we conducted a retrospective, population-based cohort study of long-term residents of Toronto, aged 35 to 100 years, who were registered for provincial publicly funded health insurance, and were without a history of hypertension (n=701 174) or diabetes mellitus (n=914 607). Road traffic noise exposure levels were assessed by the equivalent continuous A-weighted sound pressure level (dBA) for the 24-hour day and the equivalent continuous A-weighted sound pressure level for the night (11 pm-7am). Noise exposures were assigned to subjects according to their annual residential postal codes during the 15-year follow-up. We used random-effect Cox proportional hazards models adjusting for personal and area-level characteristics. From 2001 to 2015, each interquartile range increase in the equivalent continuous A-weighted sound pressure level (dBA) for the 24-hour day (10.0 dBA) was associated with an 8% increase in incident diabetes mellitus (95% CI, 1.07-1.09) and a 2% increase in hypertension (95% CI, 1.01-1.03). We obtained similar estimates with the equivalent continuous A-weighted sound pressure level for the night (11 pm-7am). These results were robust to all sensitivity analyses conducted, including further adjusting for traffic-related air pollutants (ultrafine particles and nitrogen dioxide). For both hypertension and diabetes mellitus, we observed stronger associations with the equivalent continuous A-weighted sound pressure level (dBA) for the 24-hour day among women and younger adults (aged <60 years). Conclusions Long-term exposure to road traffic noise was associated with an increased incidence of diabetes mellitus and hypertension in Toronto.

Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities.

Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.

"People like us": spatialised notions of health, stigma, power and subjectivity among women in street sex work.

Most spatially-oriented studies about health, safety and service provision among women in street sex work have taken place in large urban cities and document how the socio-legal and moral surveillance of geographical spaces constrain their daily movements and compromise their ability to care for themselves. Designed to contribute new knowledge about the broader socio-cultural and environmental landscape of sex work in smaller urban centres, we conducted qualitative interviews and social mapping activities with thirty-three women working in a medium-sized Canadian city. Our findings demonstrate a socio-spatial convergence regarding service provision, violence, and stigma, which is common in sex trading spaces that double as service landscapes for poor populations. Women in this study employ unique agential strategies to navigate these competing forces, many of which draw upon the multivalent uses of different urban spaces to optimise service access, reduce the propensity for violence, and manage their health with dignity. Their use of the spatialised term 'everywhere' as an idiom of distress regarding issues of power, agency and their desire to take part in wider civic discourse are also explored. These data contribute new insights about spatialised notions of health, stigma, agency, and subjectivity among women in sex work and how they manage 'risky' environments.

Capturing the spatial variability of noise levels based on a short-term monitoring campaign and comparing noise surfaces against personal exposures collected through a panel study.

Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LAeq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R2 (0.44-0.64), deviance (51-72%) and Akaike information criterion (AIC) (469.2-434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.

A combined emission and receptor-based approach to modelling environmental noise in urban environments.

The state of practice for noise assessment utilizes established standards for emission and propagation modelling of linear and point sources. Recently, land use regression (LUR) modelling has emerged as an alternative method due to relatively low data and computing resource demands. However, a limitation of LUR modelling is that is does not account for noise attenuation and reflections by features of the built environment. This study demonstrates and validates a method that combines the two modelling frameworks to exploit their respective strengths: Emission and propagation based prediction of traffic noise, the predominant source of noise at the level of streetscapes, and a LUR-based correction for noise sources that vary on spatial scales beyond the streetscape. Multi-criteria analysis, location-allocation modelling and stakeholder consultation identified 220 monitoring sites with optimal coverage for a 1-week sampling period. A subset of sites was used to validate a road traffic noise emission and propagation model and to specify a LUR model that predicted the contribution of other sources. The equivalent 24-h sound pressure level (LAeq) for all sites was 62.9 dBA (SD 6.4). This varied by time of day, weekday, types of roads and land uses. The traffic noise emission model demonstrated a high level of covariance with observed noise levels, with R2 values of 0.58, 0.60 and 0.59 for daytime, nighttime and 24-h periods, respectively. Combined with LUR models to correct for other noise sources, the hybrid models R2 values were 0.64, 0.71 and 0.67 for the respective time periods. The study showed that road traffic noise emissions account for most of the variability of total environmental noise in Toronto. The combined approach to predict fine resolution noise exposures with emission and receptor-based models presents an effective alternative to noise modelling approaches based on emission and propagation or LUR modelling.

Cumulative effects of noise and odour annoyances on environmental and health related quality of life.

Noise and odour annoyances are important considerations in research on health effects of air pollution and traffic noise. Cumulative exposures can occur via several chemical hazards or a combination of chemical and stressor-based hazards, and related health outcomes can be generalized as manifestations of physiological and/or psychological stress responses. A major research challenge in this field is to understand the combined health effects of physiological and psychological responses to exposure. The SF-12 Health Survey is a health related quality of life (HRQoL) instrument designed for the assessment of functional mental and physical health in clinical practice and therefore well suited to research on physiological health outcomes of exposure. However, previous research has not assessed its sensitivity to psychological stress as measured by noise annoyance and odour annoyance. The current study validated and tested this application of the SF-12 Health Survey in a cross-sectional study (n = 603) that included exposure assessment for traffic noise and air pollution in Windsor, Ontario, Canada. The results indicated that SF-12 scores in Windsor were lower than Canadian normative data. A structural equation model demonstrated that this was partially due to noise and odour annoyances, which were associated with covarying exposures to ambient nitrogen dioxide and traffic noise. More specifically, noise annoyance had a significant and negative effect on both mental and physical health factors of the SF-12 and there was a significant covariance between noise annoyance and odour annoyance. The study confirmed a significant effect of psychological responses to cumulative exposures on HRQoL. The SF-12 Health Survey shows promise with respect to assessing the cumulative health effects of outdoor air pollution and traffic noise.

Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment.

Land use regression (LUR) modeling is an effective method for estimating fine-scale distributions of ambient air pollutants. The objectives of this study are to advance the methodology for use in urban environments with relatively low levels of industrial activity and provide exposure assessments for research on health effects of air pollution. Intraurban distributions of nitrogen dioxide (NO2) and the volatile organic compounds (VOCs) benzene, toluene and m- and p-xylene were characterized based on spatial monitoring and LUR modeling in Ottawa, Ontario, Canada. Passive samplers were deployed at 50 locations throughout Ottawa for two consecutive weeks in October 2008 and May 2009. Land use variables representing point, area and line sources were tested as predictors of pooled pollutant distributions. LUR models explained 96% of the spatial variability in NO2 and 75-79% of the variability in the VOC species. Proximity to highways, green space, industrial and residential land uses were significant in the final models. More notably, proximity to industrial point sources and road network intersections were significant predictors for all pollutants. The strong contribution of industrial point sources to VOC distributions in Ottawa suggests that facility emission data should be considered whenever possible. The study also suggests that proximity to road network intersections may be an effective proxy in areas where reliable traffic data are not available.

Extricating sex and gender in air pollution research: a community-based study on cardinal symptoms of exposure.

This study investigated sex and gender differences in cardinal symptoms of exposure to a mixture of ambient pollutants. A cross sectional population-based study design was utilized in Sarnia, ON, Canada. Stratified random sampling in census tracts of residents aged 18 and over recruited 804 respondents. Respondents completed a community health survey of chronic disease, general health, and socioeconomic indicators. Residential concentrations of NO2, SO2, benzene, toluene, ethylbenzene and o/m/p-xylene were estimated by land use regression on data collected through environmental monitoring. Classification and Regression Tree (CART) analysis was used to identify variables that interacted with sex and cardinal symptoms of exposure, and a series of logistic regression models were built to predict the reporting of five or more cardinal symptoms (5+ CS). Without controlling for confounders, higher pollution ranks increased the odds ratio (OR) of reporting 5+ CS by 28% (p < 0.01; Confidence Interval (CI): 1.07-1.54). Females were 1.52 (p < 0.05; CI: 1.03-2.26) times more likely more likely to report 5+ CS after controlling for income, age and chronic diseases. The CART analysis showed that allergies and occupational exposure classified the sample into the most homogenous groups of males and females. The likelihood of reporting 5+ CS among females was higher after stratifying the sample based on occupational exposure. However, stratifying by allergic disease resulted in no significant sex difference in symptom reporting. The results confirmed previous research that found pre-existing health conditions to increase susceptibility to ambient air pollution, but additionally indicated that stronger effects on females is partly due to autoimmune disorders. Furthermore, gender differences in occupational exposure confound the effect size of exposure in studies based on residential levels of air pollution.

Assessing the spatial distribution of nitrogen dioxide in London, Ontario.

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO2) and to identify local sources influencing NO2 concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May-June 2010. NO2 concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO2 concentrations in the final LUR model, which explained 78% of NO2 variability in London. Traffic and dwelling density explained most of the variation in NO2 concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO2 variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

A geographical analysis of breast cancer clustering in southern Ontario: generating hypotheses on environmental influences.

This article presents the results of spatial analysis of breast cancer clustering in southern Ontario. Data from the Cancer Care Ontario were analyzed using the Scan Statistic at the level of county, with further analysis conducted within counties that were identified as primary clusters at the dissemination area level. The results identified five counties as primary clusters of women diagnosed with breast cancer between 1986 and 2002: Essex (relative risk [RR] =1.096-1.061; p<0.001), Lambton (RR=1.05-1.167), Chatham-Kent (RR=1.133-1.191), Niagara (RR=1.228-1.290) and Toronto (RR=1.152-1.146). The within county analysis revealed several DAs with significantly higher (RR>3, p<0.05) rates of breast cancer, and supports our hypothesis that breast cancer risk in southern Ontario may be associated with industrial and environmental (such as pesticides) pollutants. Further research is needed to verify the environmental links within the identified clusters.

Air pollution and general practitioner access and utilization: a population based study in Sarnia, 'Chemical Valley,' Ontario.

BACKGROUND: Health impacts of poor environmental quality have been identified in studies around the world and in Canada. While many of the studies have identified associations between air pollution and mortality or morbidity, few have focused on the role of health care as a potential moderator of impacts. This study assessed the determinants of health care access and utilization in the context of ambient air pollution in Sarnia, Ontario, Canada. METHODS: Residents of Sarnia participated in a Community Health Study administered by phone, while several ambient air pollutants including nitrogen dioxide (NO2), sulphur dioxide (SO2) and the volatile organic compounds benzene, toluene, ethylbenzene, mp- and o-xylene (BTEX) were monitored across the city. Land Use Regression models were used to estimate individual exposures to the measured pollutants and logistic regression models were utilized to assess the relative influence of environmental, socioeconomic and health related covariates on general practitioner access and utilization outcomes. RESULTS: The results show that general practitioner use increased with levels of exposure to nitrogen dioxide (NO2- Odds Ratio [OR]: 1.16, p < 0.05) and sulphur dioxide (SO2- OR: 1.61, p < 0.05). Low household income was a stronger predictor of having no family doctor in areas exposed to high concentrations of NO2 and SO2. Respondents without regular care living in high pollution areas were also more likely to report travelling or waiting for care in excess of 20 minutes (OR: 3.28, p < 0.05) than their low exposure counterparts (OR: 1.11, p > 0.05). CONCLUSIONS: This study provides evidence for inequitable health care access and utilization in Sarnia, with particular relevance to its situation as a sentinel high exposure environment. Levels of exposure to pollution appears to influence utilization of health care services, but poor access to primary health care services additionally burden certain groups in Sarnia, Ontario, Canada.