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Previous research has associated snowfall with risk of myocardial infarction (MI). Most studies have been conducted in regions with harsh winters; it remains unclear whether snowfall is associated with risk of MI in regions with milder or more varied climates. A case-crossover design was used to investigate the association between snowfall and death from MI in British Columbia, Canada. Deaths from MI among British Columbia residents between October 15 and March 31 from 2009 to 2017 were identified. The day of each death from MI was treated as the case day, and each case day was matched to control days drawn from the same day of the week during the same month. Daily snowfall amount was assigned to case and control days at the residential address, using weather stations within 15 km of the residence and 100 m in elevation. In total, 3,300 MI case days were matched to 10,441 control days. Compared with days that had no snowfall, odds of death from MI increased 34% (95% confidence interval: 0%, 80%) on days with heavy snowfall (≥5 cm). In stratified analysis of deaths from MI as a function of both maximum temperature and snowfall, risk was significantly increased on snowfall days when the temperature was warmer.

Climate change is an increasingly important public health issue, reflected in morbidity and mortality outcomes during extreme heat events. At the same time, the harms of social isolation with respect to a wide range of health outcomes are becoming better understood. Given that older adults are at higher risk during hot weather and at higher risk of social isolation, they are among those at highest risk for adverse impacts of extreme heat events. While specific strategies to reduce heat exposure have been described in the literature and promoted in public health practice, these may not be readily available to socially isolated older adults. As such, it is crucial to identify key approaches to address risk due to social isolation in the aging population, and to acknowledge their limitations and barriers. Interventions rooted in social connection, a concept widely applied in interventions for public health and social well-being, should be applied as a tool for adaptation to extreme heat events.RéSUMé: Le changement climatique est un problème de santé publique de plus en plus important, qui se reflète dans les résultats de la morbidité et de la mortalité lors des épisodes de chaleur extrême. Au même temps, les mals de l'isolement social en ce qui concerne un large éventail de résultats pour la santé sont de mieux en mieux compris. Étant donné que les personnes âgées sont plus à risque durant les temps chaud et à risque d'isolement social, elles font partie des personnes les plus exposées aux effets négatifs d'événements de chaleur extrême. Bien que des stratégies spécifiques pour réduire l'exposition à la chaleur aient été décrites dans la littérature et promues dans la pratique de la santé publique, elles peuvent ne pas être facilement accessibles aux personnes âgées socialement isolées. Comme tel, il est essentiel d'identifier les approches clés pour réduire les risques dus à l'isolement social de la population vieillissante et de reconnaître leurs limites et obstacles. Les interventions enracinées dans la connexion sociale, un concept largement appliqué dans les interventions pour la santé publique et le bien-être social, devraient être appliquées comme un outil d'adaptation aux événements de chaleur extreme.

Wildfire smoke exposure is associated with a range of acute health outcomes, which can be more severe in individuals with underlying health conditions. Currently, there is limited information on the susceptibility of healthcare facilities to smoke infiltration. As part of a larger study to address this gap, a rehabilitation facility in Vancouver, Canada was outfitted with one outdoor and seven indoor low-cost fine particulate matter (PM2.5) sensors in Air Quality Eggs (EGG) during the summer of 2020. Raw measurements were calibrated using temperature, relative humidity, and dew point derived from the EGG data. The infiltration coefficient was quantified using a distributed lag model. Indoor concentrations during the smoke episode were elevated throughout the building, though non-uniformly. After censoring indoor-only peaks, the average infiltration coefficient (range) during typical days was 0.32 (0.22–0.39), compared with 0.37 (0.31–0.47) during the smoke episode, a 19% increase on average. Indoor PM2.5 concentrations quickly reflected outdoor conditions during and after the smoke episode. It is unclear whether these results will be generalizable to other years due to COVID-related changes to building operations, but some of the safety protocols may offer valuable lessons for future wildfire seasons. For example, points of building entry and exit were reduced from eight to two during the pandemic, which likely helped to protect the building from wildfire smoke infiltration. Overall, these results demonstrate the utility of indoor low-cost sensors in understanding the impacts of extreme smoke events on facilities where highly susceptible individuals are present. Furthermore, they highlight the need to employ interventions that enhance indoor air quality in such facilities during smoke events.

BACKGROUND AND AIM: A growing body of evidence indicates that exposure to air pollution affects cognitive performance; however, few studies have assessed this in the context of repeated measures wi...

BACKGROUND AND AIM: There is increasing evidence that air pollution exposure adversely impacts cognitive health. While wildfire smoke is a pollutant of growing concern, there is little evidence to ...

OBJECTIVE: Wildfire smoke is an important source of air pollution associated with a range of cardiopulmonary health conditions. The Air Quality Health Index (AQHI) is the most widely used tool in Canada to communicate with the public about air pollution, but it may not adequately reflect health risks from wildfire smoke. The objective of this study was to evaluate the ability of the AQHI and four alternate AQHI-Plus amendments to predict adverse population health effects from wildfire smoke. METHODS: The maximum 1-h values of the AQHI and the four amendments were calculated for each 48-h period of the wildfire seasons from 2010 to 2017 for 32 health units in British Columbia. Generalized Poisson models were used to estimate the association between these values and daily counts of five health outcomes: all-cause mortality; physician visits for all circulatory causes; visits for all respiratory causes, including asthma; asthma-specific visits; and dispensations of salbutamol sulfate (i.e., Ventolin®). Model fit was evaluated with the Akaike information criterion. RESULTS: The AQHI and the four amendments were all associated with all five health outcomes. The AQHI exhibited best fit to the all-cause mortality and circulatory physician visits during all wildfire seasons, while the 1-h PM(2.5)Only AQHI-Plus exhibited best fit to the asthma-related outcomes during all wildfire seasons. CONCLUSION: Individuals with common respiratory conditions such as asthma and chronic obstructive pulmonary disease are particularly susceptible to wildfire smoke. As such, the 1-h PM(2.5)Only AQHI-Plus amendment was recommended for communicating about potential health effects of air quality during wildfire seasons in BC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-019-00237-w) contains supplementary material, which is available to authorized users.

Background Indoor and outdoor fine particulate matter (PM2.5) are both leading risk factors for death and disease, but making indoor measurements is often infeasible for large study populations. Methods We developed models to predict indoor PM2.5 concentrations for pregnant women who were part of a randomized controlled trial of portable air cleaners in Ulaanbaatar, Mongolia. We used multiple linear regression (MLR) and random forest regression (RFR) to model indoor PM2.5 concentrations with 447 independent 7-day PM2.5 measurements and 87 potential predictor variables obtained from outdoor monitoring data, questionnaires, home assessments, and geographic data sets. We also developed blended models that combined the MLR and RFR approaches. All models were evaluated in a 10-fold cross-validation. Results The predictors in the MLR model were season, outdoor PM2.5 concentration, the number of air cleaners deployed, and the density of gers (traditional felt-lined yurts) surrounding the apartments. MLR and RFR had similar performance in cross-validation (R2 = 50.2%, R2 = 48.9% respectively). The blended MLR model that included RFR predictions had the best performance (cross validation R2 = 81.5%). Intervention status alone explained only 6.0% of the variation in indoor PM2.5 concentrations. Conclusions We predicted a moderate amount of variation in indoor PM2.5 concentrations using easily obtained predictor variables and the models explained substantially more variation than intervention status alone. While RFR shows promise for modelling indoor concentrations, our results highlight the importance of out-of-sample validation when evaluating model performance. We also demonstrate the improved performance of blended MLR/RFR models in predicting indoor air pollution.

Objectives To estimate the proportion of the Canadian population that is more susceptible to adverse effects of ozone (O3) and fine particle (PM2.5) air pollution exposure and how this varies by health region alongside ambient concentrations of O3 and PM2.5. Methods Using data from the census, the Canadian Community Health Survey, vital statistics and published literature, we generated cross-sectional estimates for 2014 of the proportions of the Canadian population considered more susceptible due to age, chronic disease, pregnancy, outdoor work, socio-economic status, and diet. We also estimated 2010–2012 average concentrations of O3 and PM2.5. Analyses were conducted nationally and for 110 health regions. Results Restrictive criteria (age

Supplemental Digital Content is available in the text., Background: Ambulance data provide a useful source of population-based and spatiotemporally resolved information for assessing health impacts of air pollution in nonhospital settings. We used the clinical records of paramedics to quantify associations between particulate matter (PM2.5) and diabetic, cardiovascular, and respiratory conditions commonly managed by those responding to calls for emergency ambulance services. Methods: We evaluated 394,217 paramedic assessments from three states in Southeastern Australia (population 13.2 million) and daily PM2.5 concentrations modeled at 5 km resolution from 2009 to 2014. We used a time-stratified, case-crossover analysis adjusted for daily meteorology to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for each clinical outcome per 10 µg/m3 increase in daily PM2.5 at lags from 0 to 2 days. Results: Increased PM2.5 was associated with increased odds of paramedic assessments of hypoglycemia (OR = 1.07; 95% CI = 1.02, 1.12, lag 0), arrhythmia (OR = 1.05; 95% CI = 1.02, 1.09, lag 0), heart failure (OR = 1.07; 95% CI = 1.02, 1.12, lag 1), faint (OR = 1.09; 95% CI = 1.04–1.13, lag 0), asthma (OR = 1.06; 95% CI = 1.01, 1.11, lag 1), chronic obstructive pulmonary disease (OR = 1.07; 95% CI = 1.01, 1.13, lag 1), and croup (OR = 1.09; 95% CI = 1.02, 1.17). We did not identify associations with cerebrovascular outcomes. Conclusions: Ambulance data enable the evaluation of important clinical syndromes that are often initially managed in nonhospital settings. Daily PM2.5 was associated with hypoglycemia, faint, and croup in addition to the respiratory and cardiovascular outcomes that are better established.

Wildland fires are diminishing air quality on a seasonal and regional basis, raising concerns about respiratory health risks to the public and occupational groups. This American Thoracic Society (ATS) workshop was convened in 2019 to meet the growing health threat of wildland fire smoke. The workshop brought together a multidisciplinary group of 19 experts, including wildland fire managers, public health officials, epidemiologists, toxicologists, and pediatric and adult pulmonologists. The workshop examined the following four major topics: 1) the science of wildland fire incidence and fire management, 2) the respiratory and cardiovascular health effects of wildland fire smoke exposure, 3) communication strategies to address these health risks, and 4) actions to address wildland fire health impacts. Through formal presentations followed by group discussion, workshop participants identified top priorities for fire management, research, communication, and public policy to address health risks of wildland fires. The workshop concluded that short-term exposure to wildland smoke causes acute respiratory health effects, especially among those with asthma and chronic obstructive pulmonary disease. Research is needed to understand long-term health effects of repeated smoke exposures across fire seasons for children, adults, and highly exposed occupational groups (especially firefighters). Other research priorities include fire data collection and modeling, toxicology of different fire fuel sources, and the efficacy of health protective measures to prevent respiratory effects of smoke exposure. The workshop committee recommends a unified federal response to the growing problem of wildland fires, including investment in fire behavior and smoke air quality modeling, research on the health impacts of smoke, and development of robust clinical and public health communication tools.

Background: The modular British Columbia Asthma Prediction System (BCAPS) is designed to reduce information burden during wildfire smoke events by automatically gathering, integrating, generating, and visualizing data for public health users. The BCAPS framework comprises five flexible and geographically scalable modules: (1) historic data on fine particulate matter (PM2.5) concentrations; (2) historic data on relevant health indicator counts; (3) PM2.5 forecasts for the upcoming days; (4) a health forecasting model that uses the relationship between (1) and (2) to predict the impacts of (3); and (5) a reporting mechanism.Methods: The 2018 wildfire season was the most extreme in British Columbia history. Every morning BCAPS generated forecasts of salbutamol sulfate (e.g., Ventolin) inhaler dispensations for the upcoming days in 16 Health Service Delivery Areas (HSDAs) using random forest machine learning. These forecasts were compared with observations over a 63-day study period using different methods including the index of agreement (IOA), which ranges from 0 (no agreement) to 1 (perfect agreement). Some observations were compared with the same period in the milder wildfire season of 2016 for context.Results: The mean province-wide population-weighted PM2.5 concentration over the study period was 22.0 μg/m3, compared with 4.2 μg/m3 during the milder wildfire season of 2016. The PM2.5 forecasts underpredicted the severe smoke impacts, but the IOA was relatively strong with a population-weighted average of 0.85, ranging from 0.65 to 0.95 among the HSDAs. Inhaler dispensations increased by 30% over 2016 values. Forecasted dispensations were within 20% of the observed value in 71% of cases, and the IOA was strong with a population-weighted average of 0.95, ranging from 0.92 to 0.98. All measures of agreement were correlated with HSDA population, where BCAPS performance was better in the larger populations with more moderate smoke impacts. The accuracy of the health forecasts was partially dependent on the accuracy of the PM2.5 forecasts, but they were robust to over- and underpredictions of PM2.5 exposure.Conclusions: Daily reports from the BCAPS framework provided timely and reasonable insight into the population health impacts of predicted smoke exposures, though more work is necessary to improve the PM2.5 and health indicator forecasts.

As we enter the wildfire season in the northern hemisphere, the potential for a dangerous interaction between SARS-CoV-2 and smoke pollution should be recognized and acknowledged. This is challenging because the public health threat of COVID-19 is immediate and clear, whereas the public health threat of wildfire smoke seems distant and uncertain in comparison. However, we must start preparing now to effectively manage the combination of public health threats that we may face in the months ahead. (Am J Public Health. Published online ahead of print June 18, 2020: e1-e3. doi:10.2105/AJPH.2020.305744).

Background Harmful algal blooms produce paralytic shellfish toxins that accumulate in the tissues of filter feeding shellfish. Ingestion of these toxic shellfish can cause a serious and potentially fatal condition known as paralytic shellfish poisoning (PSP). The coast of British Columbia is routinely monitored for shellfish toxicity, and this study uses data from the monitoring program to identify spatiotemporal patterns in shellfish toxicity events and their relationships with environmental variables. Methods The dinoflagellate genus Alexandrium produces the most potent paralytic shellfish toxin, saxitoxin (STX). Data on all STX measurements were obtained from 49 different shellfish monitoring sites along the coast of British Columbia for 2002–2012, and monthly toxicity events were identified. We performed hierarchical cluster analysis to group sites that had events in similar areas with similar timing. Machine learning techniques were used to model the complex relationships between toxicity events and environmental variables in each group. Results The Strait of Georgia and the west coast of Vancouver Island had unique toxicity regimes. Out of the seven environmental variables used, toxicity in each cluster could be described by multivariable models including monthly sea surface temperature, air temperature, sea surface salinity, freshwater discharge, upwelling, and photosynthetically active radiation. The sea surface salinity and freshwater discharge variables produced the strongest univariate models for both geographic areas. Conclusions Applying these methods in coastal regions could allow for the prediction of shellfish toxicity events by environmental conditions. This has the potential to optimize biotoxin monitoring, improve public health surveillance, and engage the shellfish industry in helping to reduce the risk of PSP.

There is limited evidence for short-term association between mortality and ambient air pollution in the Middle East and no study has evaluated exposure windows of about a month prior to death. We investigated all-cause non-accidental daily mortality and its association with fine particulate matter (PM; 2.5; ), nitrogen dioxide (NO; 2; ), and the Air Quality Index (AQI) from March 2011 through March 2014 in the megacity of Tehran, Iran. Generalized additive quasi-Poisson models were used within a distributed lag linear modeling framework to estimate the cumulative effects of PM; 2.5; , NO; 2; , and the AQI up to a lag of 45 days. We further conducted multi-pollutant models and also stratified the analyses by sex, age group, and season. The relative risk (95% confidence interval (CI)) for all seasons, both sexes and all ages at lag 0 for PM; 2.5; , NO; 2; , and AQI were 1.004 (1.001, 1.007), 1.003 (0.999, 1.007), and 1.004 (1.001, 1.007), respectively, per inter-quartile range (IQR) increment (18.8 μg/m; 3; for PM; 2.5; , 12.6 ppb for NO; 2; , and 31.5 for AQI). In multi-pollutant models, the PM; 2.5; associations were almost independent from NO; 2; . However, the RRs for NO; 2; were slightly attenuated after adjustment for PM; 2.5; but they were still largely independent from PM; 2.5; . The cumulative relative risks (95% CI) per IQR increment reached maximum during the cooler months, including: 1.13 (1.06, 1.20) for PM; 2.5; at lag 0-31 (for females, all ages); 1.17 (1.10, 1.25) for NO; 2; at lag 0-45 (for males, all ages); and 1.13 (1.07, 1.20) for the AQI at lag 0-30 (for females, all ages). Generally, the RRs were slightly larger for NO; 2; than PM; 2.5; and AQI. We found somewhat larger RRs in females, age group >65 years of age, and in cooler months. In summary, positive associations were found in most models. This is the first study to report short-term associations between all-cause non-accidental mortality and ambient PM; 2.5; and NO; 2; in Iran.

This paper outlines a latent process model for forecasting multiple health outcomes arising from a common environmental exposure. Traditionally, surveillance models in environmental health do not link health outcome measures, such as morbidity or mortality counts, to measures of exposure, such as air pollution. Moreover, different measures of health outcomes are treated as independent, while it is known that they are correlated with one another over time as they arise in part from a common underlying exposure. We propose modelling an environmental exposure as a latent process, and we describe the implementation of such a model within a hierarchical Bayesian framework and its efficient computation using integrated nested Laplace approximations. Through a simulation study, we compare distinct univariate models for each health outcome with a bivariate approach. The bivariate model outperforms the univariate models in bias and coverage of parameter estimation, in forecast accuracy and in computational efficiency. The methods are illustrated with a case study using healthcare utilization and air pollution data from British Columbia, Canada, 2003-2011, where seasonal wildfires produce high levels of air pollution, significantly impacting population health. Copyright © 2016 John Wiley & Sons, Ltd.

Smoke from wildfires contains many air pollutants of concern and epidemiological studies have identified associations between exposure to wildfire smoke PM2.5 and mortality and respiratory morbidity, and a possible association with cardiovascular morbidity. For this study, a retrospective analysis of air quality modelling was performed to quantify the exposure to wildfire-PM2.5 across the Canadian population. The model included wildfire emissions from across North America for a 5-month period from May to September (i.e. wildfire season), between 2013 and 2015 and 2017-2018. Large variations in wildfire-PM2.5 were noted year-to-year, geospatially, and within fire season. The model results were then used to estimate the national population health impacts attributable to wildfire-PM2.5 and the associated economic valuation. The analysis estimated annual premature mortalities ranging from 54-240 premature mortalities attributable to short-term exposure and 570-2500 premature mortalities attributable to long-term exposure, as well as many non-fatal cardiorespiratory health outcomes. The economic valuation of the population health impacts was estimated per year at $410M-$1.8B for acute health impacts and $4.3B-$19B for chronic health impacts for the study period. The health impacts were greatest in the provinces with populations in close proximity to wildfire activity, though health impacts were also noted across many provinces indicating the long-range transport of wildfire-PM2.5. Understanding the population health impacts of wildfire smoke is important as climate change is anticipated to increase wildfire activity in Canada and abroad.

Background: Indoor and outdoor exposures to fine particulate matter (PM2.5) are both leading risk factors for death and disease, but collecting indoor measurements is often infeasible for large stu...

There is limited evidence for short-term associations between mortality and ambient air pollution in the Middle East, and no studies examining the association between daily mortality and the air qu...

Increases in wildfire frequency and intensity have been observed over recent decades, and are expected to continue with climate change. Exposure to wildfire smoke has been associated with a wide ra...

In a rapidly urbanizing world, identifying evidence-based strategies to support healthy design is essential. Although urban living offers increased access to critical resources and can help to mitigate climate change, densely populated neighborhood environments are often higher in many of the physical and psychological stressors that are detrimental to health, and lower in the social capital that is beneficial to health. One component of urban form that can reduce these stressors and improve social capital is nature: greenspace, such as parks and street trees, and bluespace, such as rivers and oceans. In this project, we applied measures from a Natural Space Index previously developed for the Vancouver, Canada census metropolitan area to explore the relationship between distinct measures of natural space and prevalence of (1) major depressive disorder, (2) negative mental health, and (3) psychological distress. In addition, we examined direct associations between natural space exposure and neighborhood social capital, as measured via self-reported sense of community belonging (SoC), as well as the potential mental health benefits of natural space mediated via SoC. Using data from the population-based, cross-sectional 2012 Canadian Community Health Survey-Mental Health (weighted n = 1,930,048), we found no direct associations between any measure of natural space and mental health in models adjusted for 11 demographic, socioeconomic, household arrangement, health, and urban design variables. However, publicly accessible neighborhood nature was associated with increased odds of higher SoC. A 1% increase in the percentage of natural space (combined greenspace and bluespace) within 500 m had an odds ratio [95% confidence interval] of 1.05 [1.00, 1.10] for very strong vs. very weak SoC and 1.04 [1.01, 1.08] for somewhat strong vs. very weak SoC. In addition, higher levels of SoC were associated with improvements in all three mental health outcomes. Mediation tests indicated significant indirect effects of both publicly accessible neighborhood nature variables on reductions in psychological distress and reduced odds of negative mental health via increased sense of SoC. This suggests that natural space has the potential to address the pressing issue of social isolation and, in turn, poor mental health faced by residents of dense urban environments.

Aeroallergens occur naturally in the environment and are widely dispersed across Canada, yet their public health implications are not well-understood. This review intends to provide a scientific and public health-oriented perspective on aeroallergens in Canada: their distribution, health impacts, and new developments including the effects of climate change and the potential role of aeroallergens in the development of allergies and asthma. The review also describes anthropogenic effects on plant distribution and diversity, and how aeroallergens interact with other environmental elements, such as air pollution and weather events. Increased understanding of the relationships between aeroallergens and health will enhance our ability to provide accurate information, improve preventive measures and provide timely treatments for affected populations.

Background: Data from poison centers have the potential to be valuable for public health surveillance of long-term trends, short-term aberrations from those trends, and poisonings occurring in near-real-time. This information can enable long-term prevention via programs and policies and short-term control via immediate public health response. Over the past decade, there has been an increasing use of poison control data for surveillance in the United States, Europe, and New Zealand, but this resource still remains widely underused. Objective: The British Columbia (BC) Drug and Poison Information Centre (DPIC) is one of five such services in Canada, and it is the only one nested within a public health agency. This study aimed to demonstrate how DPIC data are used for routine public health surveillance in near-real-time using the case study of its alerting system for illness related to consumption of shellfish (ASIRCS). Methods: Every hour, a connection is opened between the WBM software Visual Dotlab Enterprise, which holds the DPIC database, and the R statistical computing environment. This platform is used to extract, clean, and merge all necessary raw data tables into a single data file. ASIRCS automatically and retrospectively scans a 24-hour window within the data file for new cases related to illnesses from shellfish consumption. Detected cases are queried using a list of attributes: the caller location, exposure type, reasons for the exposure, and a list of keywords searched in the clinical notes. The alert generates a report that is tailored to the needs of food safety specialists, who then assess and respond to detected cases. Results: The ASIRCS system alerted on 79 cases between January 2015 and December 2016, and retrospective analysis found 11 cases that were missed. All cases were reviewed by food safety specialists, and 58% (46/79) were referred to designated regional health authority contacts for follow-up. Of the 42% (33/79) cases that were not referred to health authorities, some were missing follow-up information, some were triggered by allergies to shellfish, and some were triggered by shellfish-related keywords appearing in the case notes for nonshellfish-related cases. Improvements were made between 2015 and 2016 to reduce the number of cases with missing follow-up information. Conclusions: The surveillance capacity is evident within poison control data as shown from the novel use of DPIC data for identifying illnesses related to shellfish consumption in BC. The further development of surveillance programs could improve and enhance response to public health emergencies related to acute illnesses, chronic diseases, and environmental exposures. [JMIR Public Health Surveill 2018;4(1):e17]

The British Columbia Asthma Monitoring System (BCAMS) tracks forest fire smoke exposure and asthma-related health outcomes, identifying excursions beyond expected daily counts. Weekly reports during the wildfire season support public health and emergency management decision-making. We evaluated BCAMS by identifying excursions for asthma-related physician visits and dispensations of the reliever medication salbutamol sulfate and examining their corresponding smoke exposures. A disease outbreak detection algorithm identified excursions from 1 July to 31 August 2014. Measured, modeled, and forecasted concentrations of fine particulate matter (PM2.5) were used to assess exposure. We assigned PM2.5 levels to excursions by choosing the highest value within a seven day window centred on the excursion day. Smoky days were defined as those with PM2.5 levels ≥ 25 µg/m3. Most excursions (57%–71%) were assigned measured or modeled PM2.5 concentrations of 10 µg/m3 or higher. Of the smoky days, 55.8% and 69.8% were associated with at least one excursion for physician visits and salbutamol dispensations, respectively. BCAMS alerted most often when measures of smoke exposure were relatively high. Better performance might be realized by combining asthma-related outcome metrics in a bivariate model.

Drinking water related infections are expected to increase in the future due to climate change. Understanding the current links between these infections and environmental factors is vital to understand and reduce the future burden of illness. We investigated the relationship between weekly reported cryptosporidiosis and giardiasis (n = 7,422), extreme precipitation (>90th percentile), drinking water turbidity, and preceding dry periods in a drinking water system located in greater Vancouver, British Columbia, Canada (1997–2009) using distributed lag non-linear Poisson regression models adjusted for seasonality, secular trend, and the effect of holidays on reporting. We found a significant increase in cryptosporidiosis and giardiasis 4–6 weeks after extreme precipitation. The effect was greater following a dry period. Similarly, extreme precipitation led to significantly increased turbidity only after prolonged dry periods. Our results suggest that the risk of cryptosporidiosis and giardiasis increases with extreme precipitation, and that the effects are more pronounced after a prolonged dry period. Given that extreme precipitation events are expected to increase with climate change, it is important to further understand the risks from these events, develop planning tools, and build resilience to these future risks.

FireWork is an on-line, one-way coupled meteorology–chemistry model based on near-real-time wildfire emissions. It was developed by Environment and Climate Change Canada to deliver operational real-time forecasts of biomass-burning pollutants, in particular fine particulate matter (PM2.5), over North America. Such forecasts provide guidance for early air quality alerts that could reduce air pollution exposure and protect human health. A multi-year (2013–2016) analysis of FireWork forecasts over a five-month period (May to September) was conducted. This work used an archive of FireWork outputs to quantify wildfire contributions to total PM2.5 surface concentrations across North America. Different concentration thresholds (0.2 to 28 µg/m3) and averaging periods (24 h to five months) were considered. Analysis suggested that, on average over the fire season, 76% of Canadians and 69% of Americans were affected by seasonal wildfire-related PM2.5 concentrations above 0.2 µg/m3. These effects were particularly pronounced in July and August. Futhermore, the analysis showed that fire emissions contributed more than 1 µg/m3 of daily average PM2.5 concentrations on more than 30% of days in the western USA and northwestern Canada during the fire season.

Exposure to forest fire smoke (FFS) is associated with multiple adverse health effects, mostly respiratory. Findings for cardiovascular effects have been inconsistent, possibly related to the limitations of conventional methods to assess FFS exposure. In previous work, we developed an empirical model to estimate smoke-related fine particulate matter (PM2.5) for all populated areas in British Columbia (BC), Canada. Here, we evaluate the utility of our model by comparing epidemiologic associations between modeled and measured PM2.5. For each local health area (LHA), we used Poisson regression to estimate the effects of PM2.5 estimates and measurements on counts of medication dispensations and outpatient physician visits. We then used meta-regression to estimate the overall effects. A 10 μg/m(3) increase in modeled PM2.5 was associated with increased sabutamol dispensations (RR=1.04, 95% CI 1.03-1.06), and physician visits for asthma (1.06, 1.04-1.08), COPD (1.02, 1.00-1.03), lower respiratory infections (1.03, 1.00-1.05), and otitis media (1.05, 1.03-1.07), all comparable to measured PM2.5. Effects on cardiovascular outcomes were only significant using model estimates in all LHAs during extreme fire days. This suggests that the exposure model is a promising tool for increasing the power of epidemiologic studies to detect the health effects of FFS via improved spatial coverage and resolution.

Residential exposure to radon gas is associated with increased risk of lung cancer, especially in smokers. Most evidence about the health effects of radon has been derived from meta-analyses on global epidemiologic studies, but administrative data can help public health authorities to explore the local impacts. Eighty health units in British Columbia (BC), Canada, were classified as having low, moderate, or high radon risk using more than 3,800 residential measurements. Vital statistics records were used to identify deaths due to lung cancer and to all natural causes. The annual ratio of lung cancer mortality to all natural mortality was plotted for the 1986-2012 study period for each radon classification. Visualizations were stratified by gender and by smoking prevalence. The overall ratio increased throughout the study period in high radon areas and remained stable in low and moderate radon areas. The increase was most pronounced for females, especially when plots were stratified by smoking prevalence. These limited but interesting findings confirm that radon is one risk factor for lung cancer mortality in BC and that its effects differ across gender and smoking strata. The results would be strengthened by replication, and more rigorous methods are required to assess other contributing factors.

The purpose of this study was to examine the effectiveness of food handler retraining. The food safety knowledge of handlers who had been previously certified by the FOODSAFE training programme in British Columbia, Canada was compared prior to and nine months following FOODSAFE retraining. The previously FOODSAFE trained food handlers were selected from participants of a 2009 survey. Food safety knowledge scores were compared between (1) previously FOODSAFE trained food handlers who received retraining (the intervention group), (2) previously FOODSAFE trained food handlers who did not receive retraining (the control group), and (3) untrained food handlers (the untrained group). Data gathered in a telephone survey demonstrated knowledge scores were significantly higher in the intervention group when compared with the control group, and when compared with the untrained group. No differences in food safety knowledge scores were found based on English as a first language or receiving food safety training other than FOODSAFE. These results support a requirement for periodic recertification of trained food handlers and demonstrate a need for education of untrained food handlers with a food safety training programme.

Climate change is already affecting the cardiorespiratory health of populations around the world, and these impacts are expected to increase. The present overview serves as a primer for respirologists who are concerned about how these profound environmental changes may affect their patients. The authors consider recent peer-reviewed literature with a focus on climate interactions with air pollution. They do not discuss in detail cardiorespiratory health effects for which the potential link to climate change is poorly understood. For example, pneumonia and influenza, which affect >500 million people per year, are not addressed, although clear seasonal variation suggests climate-related effects. Additionally, large global health impacts in low-resource countries, including migration precipitated by environmental change, are omitted. The major cardiorespiratory health impacts addressed are due to heat, air pollution and wildfires, shifts in allergens and infectious diseases along with respiratory impacts from flooding. Personal and societal choices about carbon use and fossil energy infrastructure should be informed by their impacts on health, and respirologists can play an important role in this discussion.

Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM2.5, BC, and UFPN data respectively, to develop separate morning (0800-1200) and afternoon (1200-1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC, and UFPN at LUR sites were 133 (96-232) μg m(-3), 11 (6-21) μg m(-3), and 40 (27-72) × 10(3) cm(-3) respectively. In addition (a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R(2) values were higher for morning (for PM2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.

The purpose of this study was to examine food safety knowledge of trained food handlers certified under the FOODSAFE training program in British Columbia, Canada and to evaluate food safety knowledge, attitudes and self-reported hand washing practices in trained and untrained food handler groups. Data gathered in a telephone survey demonstrated knowledge scores significantly decreased in FOODSAFE trained workers over a 15 year period post-certification. Knowledge scores were significantly higher in trained compared with untrained food handlers. No differences were noted between groups based on sex. Supervisory status and years of experience resulted in improved knowledge scores in both trained and untrained groups, but increasing age was important for improved knowledge in only the untrained group. Food handlers' place of employment and level of education were also associated with significantly improved knowledge scores. FOODSAFE trained food handlers' reported significantly better hand washing practices and attitudes compared with the untrained food handler group. These results support a requirement for recertification of trained food handlers and demonstrate a need for education of untrained food handlers with a food safety training program such as FOODSAFE.

Background Land-use regression (LUR) is a cost-effective approach for predicting spatial variability in ambient air pollutant concentrations with high resolution. Models have been widely used in epidemiological studies and are often applied to time periods before or after the period of air quality monitoring used in model development. However, it is unclear how well such models perform when extrapolated over time. Objective The objective of this study was to assess the temporal stability of LUR models over a period of 7 years in Metro Vancouver, Canada. Methods A set of NO and NO 2 LUR models based on 116 measurements were developed in 2003. In 2010, we made 116 measurements again, of which 73 were made at the exact same location as in 2003. We then developed 2010 models using updated data for the same predictor variables used in 2003, and also explored additional variables. Four methods were used to derive model predictions over 7 years, and predictions were compared with measurements to assess the temporal stability of LUR models. Results The correlation between 2003 NO and 2010 NO measurements was 0.87 with a mean (sd) decrease of 11.3 (9.9) ppb. For NO 2 , the correlation was 0.74, with a mean (sd) decrease of 2.4 (3.2) ppb. 2003 and 2010 LUR models explained similar amounts of spatial variation ( R 2 = 0.59 and R 2 = 0.58 for NO; R 2 = 0.52 and R 2 = 0.63 for NO 2 , in 2003 and in 2010 respectively). The 2003 models explained more variability in the 2010 measurements ( R 2 = 0.58–0.60 for NO; R 2 = 0.52–0.61 for NO 2 ) than the 2010 models explained in the 2003 measurements ( R 2 = 0.50–0.55 for NO; R 2 = 0.44–0.49 for NO 2 ), and the 2003 models explained as much variability in the 2010 measurements as they did in the 2003 measurements. Conclusion LUR models are able to provide reliable estimates over a period of 7 years in Metro Vancouver. When concentrations and their variability are decreasing over time, the predictive power of LUR models is likely to remain the same or to improve in forecasting scenarios, but to decrease in hind-casting scenarios.

Landscape fires can produce large quantities of smoke that degrade air quality in both remote and urban communities. Smoke from these fires is a complex mixture of fine particulate matter and gases, exposure to which is associated with increased respiratory and cardiovascular morbidity and mortality. The public health response to short-lived smoke events typically advises people to remain indoors with windows and doors closed, but does not emphasize the use of portable air cleaners (PAC) to create private or public clean air shelters. High efficiency particulate air filters and electrostatic precipitators can lower indoor concentrations of fine particulate matter and improve respiratory and cardiovascular outcomes. We argue that PACs should be at the forefront of the public health response to landscape fire smoke events.

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R2 values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited.

Introduction Climate change is expected to increase the burden of waterborne acute gastrointestinal illness (AGI) with the increased frequency and intensity of extreme precipitation events. Turbidity in source water is a risk factor. Here we investigate the relationship between extreme precipitation, turbidity and parasitic AGI. Further, we project the impact of climate change on these illnesses at a watershed level. Methods All 1997–2009 reported cases of cryptosporidiosis and giardiasis in a population served by a municipal surface drinking water system were analysed using distributed lag non-linear models. Precipitation was assessed for a lag up to six weeks, and adjusted for seasonality, secular trend, preceding dry/wet period and holiday effects. The mean annual case counts were predicted for 2060–2069 using downscaled daily precipitation projections from 10 global climate models under a moderate emissions growth scenario. Results Including 7422 cases, a significant increase in cryptosporidiosis and giardiasis 5–6 weeks after extreme precipitation (>90th percentile) was found during the study period. The highest rate ratio (1.17; 1.07–1.24) was identified for a lag of five weeks. A preceding dry period further increased the risk, which appears to be driven by increases in turbidity. Temperature did not contribute significantly to this risk. Climate models indicate decreases in average weekly and extreme precipitation during dry seasons in the 2060s, but increases in rainy seasons compared with 2000–2009. The overall annual disease burden increased by 6.3 % −14.2% (ensemble mean 12.1%). Discussion We found a significant risk of waterborne illness associated with extreme precipitation events in a large and well-protected municipal drinking water system. The effects were most pronounced following a dry period. To try and reduce these future risks additional filtration of finished water is being deployed for these sources. There is a need to increase resilience in water systems to address the impacts due to climate change.

Introduction: The AirCare vehicle inspection program began in 1992 to control tailpipe emissions within the Greater Vancouver area, Canada, and ended in 2014. Since 1992, there have been regional d...

Background: Emergency ambulance dispatches (EAD) are a novel outcome for evaluating the public health impacts of air pollution. We assessed the relationships between ambient particulate matter (PM) from all sources, PM from landscape fire smoke (LFS), and EADs likely to be associated with cardiorespiratory problems in the Sydney greater metropolitan region for an 11-year period from 2004 to 2015. Methods: EAD codes are assigned at the time of the call to emergency services using standard computer assisted algorithms. We assessed EADs coded as: breathing problems, chest pain, stroke or cerebrovascular accident (stroke), cardiac or respiratory arrest and death (arrest), and heart or defibrillator problems (other heart problems). Using a daily times series study design with a generalized linear Poisson regression model we quantified the association between EAD and daily PM2.5 from all sources (PM2.5,all) and PM2.5 primarily due to LFS (PM2.5,LFS). Results: Increases of 10 μg·m−3 in PM2.5,all were positively associated with same day EAD for breathing problems (RR = 1.03, 95% CI 1.02 to 1.04), arrest (RR = 1.03, 95% CI 1.00 to 1.06), and chest pain (RR = 1.01 CI 1.00 to 1.02) but not with other outcomes. Increases of 10 μg·m−3 PM2.5,LFS were also positively associated with breathing problems on the same day (RR = 1.04, 95% CI 1.02 to 1.05) and other heart problems at lag of two days (RR = 1.05, 95% CI 1.01 to 1.09). Conclusions: Emergency dispatches for breathing problems are associated with PM2.5,all and PM2.5,LFS and provide a sensitive end point for continued research and surveillance activities investigating the impacts of daily fluctuations in ambient PM2.5. Keywords: Particulate matter, Landscape fire smoke, Emergency ambulance dispatches

Background: Industrial plants emit air pollutants like fine particles (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) that may affect the health of individuals living nearby. Objective: To assess the effects of community exposure to air emissions of PM2.5, SO2, and NO2 from pulp mills, oil refineries, metal smelters, on respiratory hospital admissions in young children in Quebec (QC) and British Columbia (BC), Canada. Methods: We assessed QC, BC and pooled associations between the following estimates of exposure and hospital admissions for asthma and bronchiolitis in children aged 2–4 years for the years 2002–2010: i) Crude emission exposures at the residential postal codes of children, calculated by multiplying estimated daily emissions of PM2.5, SO2, or NO2 from all nearby (

Objectives. We assessed shifts in patterns of mortality during a hot weather event in greater Vancouver, British Columbia. Methods. We used a case-only analysis to compare characteristics of individuals who died during the hottest week of 2009 with those who died (1) during earlier summer weeks in 2009 and (2) during the same calendar weeks in the summers of 2001 through 2008. Results. Compared with the 8 previous weeks of 2009, odds of mortality during the summer’s hottest week were highest in the 65 to 74 years age category, compared with the 85 years and older category (odds ratio [OR] = 1.47; 95% confidence interval [CI] = 1.06, 2.03). The number of deaths at home increased over deaths in hospitals or institutions (OR = 1.43; 95% CI = 1.10, 1.86). Densely populated administrative health areas were more affected. Conclusions. A shift toward deaths at home suggests that in-home–based protective measures should be part of planning for hot weather events in greater Vancouver. Targeting should be considered for those aged 65 to 74 years. The case-only approach is quick and easy to apply and can provide useful information about localized, time-limited events.

Exposure to forest fire smoke is episodic, which makes its health effects challenging to study. We review the newest contributions to a growing literature on acute respiratory outcomes.Smoke exposure was associated with increases in self-reported symptoms, medication use, outpatient physician visits, emergency room visits, hospital admissions, and mortality. The associations were strongest for the outcomes most specific to asthma.Studies with varied approaches to exposure assessment and varied measures of respiratory outcomes were consistent among themselves, and consistent with most previous work.

Background: During the summer of 2003 numerous fires burned in British Columbia, Canada. Objectives: We examined the associations between respiratory and cardiovascular physician visits and hospital admissions, and three measures of smoke exposure over a 92-day study period (1 July to 30 September 2003). Methods: A population-based cohort of 281,711 residents was identified from administrative data. Spatially specific daily exposure estimates were assigned to each subject based on total measurements of particulate matter (PM) ≤ 10 μm in aerodynamic diameter (PM10) from six regulatory tapered element oscillating microbalance (TEOM) air quality monitors, smoke-related PM10 from a CALPUFF dispersion model run for the study, and a SMOKE exposure metric for plumes visible in satellite images. Logistic regression with repeated measures was used to estimate associations with each outcome. Results: The mean (± SD) exposure based on TEOM-measured PM10 was 29 ± 31 μg/m3, with an interquartile range of 14–31 μg/m3. Correlations between the TEOM, smoke, and CALPUFF metrics were moderate (0.37–0.76). Odds ratios (ORs) for a 30-μg/m3 increase in TEOM-based PM10 were 1.05 [95% confidence interval (CI), 1.03–1.06] for all respiratory physician visits, 1.16 (95% CI, 1.09–1.23) for asthma-specific visits, and 1.15 (95% CI, 1.00–1.29) for respiratory hospital admissions. Associations with cardiovascular outcomes were largely null. Conclusions: Overall we found that increases in TEOM-measured PM10 were associated with increased odds of respiratory physician visits and hospital admissions, but not with cardiovascular health outcomes. Results indicating effects of fire smoke on respiratory outcomes are consistent with previous studies, as are the null results for cardiovascular outcomes. Some agreement between TEOM and the other metrics suggests that exposure assessment tools that are independent of air quality monitoring may be useful with further refinement.

INTRODUCTION: Extreme air pollution events due to bushfire smoke and dust storms are expected to increase as a consequence of climate change, yet little has been published about their population health impacts. We examined the association between air pollution events and mortality in Sydney from 1997 to 2004. METHODS: Events were defined as days for which the 24h city-wide concentration of PM(10) exceeded the 99th percentile. All events were researched and categorised as being caused by either smoke or dust. We used a time-stratified case-crossover design with conditional logistic regression modelling adjusted for influenza epidemics, same day and lagged temperature and humidity. Reported odds ratios (OR) and 95% confidence intervals are for mortality on event days compared with non-event days. The contribution of elevated average temperatures to mortality during smoke events was explored. RESULTS: There were 52 event days, 48 attributable to bushfire smoke, six to dust and two affected by both. Smoke events were associated with a 5% increase in non-accidental mortality at a lag of 1 day OR (95% confidence interval (CI)) 1.05 (95%CI: 1.00-1.10). When same day temperature was removed from the model, additional same day associations were observed with non-accidental mortality OR 1.05 (95%CI: 1.00-1.09), and with cardiovascular mortality OR (95%CI) 1.10 (95%CI: 1.00-1.20). Dust events were associated with a 15% increase in non-accidental mortality at a lag of 3 days, OR (95%CI) 1.16 (95%CI: 1.03-1.30). CONCLUSIONS: The magnitude and temporal patterns of association with mortality were different for smoke and dust events. Public health advisories during bushfire smoke pollution episodes should include advice about hot weather in addition to air pollution.

Epidemiological studies of exposure to vegetation fire smoke are often limited by the availability of accurate exposure data. This paper describes a systematic framework for retrospectively identifying the cause of air pollution events to facilitate a long, multicenter analysis of the public health effects of vegetation fire smoke pollution in Australia. Pollution events were statistically defined as any day at or above the 95th percentile of the 24-hr average concentration of particulate matter (PM). These were identified for six cities from three distinct ecoclimatic regions of Australia. The dates of each event were then crosschecked against a range of information sources, including online newspaper archives, government and research agency records, satellite imagery, and aerosol optical thickness measures to identify the cause for the excess particulate pollution. Pollution events occurred most frequently during summer for cities in subtropical and arid regions and during winter for cities in temperate regions. A cause for high PM on 67% of days examined in the city of Sydney was found, and 94% of these could be attributed to landscape fire smoke. Results were similar for cities in other subtropical and arid locations. Identification of the cause of pollution events was much lower in colder temperate regions where fire activity is less frequent. Bushfires were the most frequent cause of extreme pollution events in cities located in subtropical and arid regions of Australia. Although identification of pollution episodes was greatly improved by the use of multiple sources of information, satellite imagery was the most useful tool for identifying bushfire smoke pollution events.

The extensible Biomass Smoke Validated Events Database is an ongoing, community driven, collection of air pollution events which are known to be caused by vegetation fires such as bushfires (also known as wildfire and wildland fires), or prescribed fuel reduction burns, and wood heaters. This is useful for researchers of health impacts who need to distinguish smoke from vegetation versus other sources. The overarching aim is to study statistical associations between biomass smoke pollution and health. Extreme pollution events may also be caused by dust storms or fossil fuel smog events and so validation is necessary to ensure the events being studied are from biomass. This database can be extended by contribution from other researchers outside the original team. There are several available protocols for adding validated smoke events to the database, to ensure standardization across datasets. Air pollution data can be included, and free software was created for identification of extreme values. Protocols are described for reference material needed as supporting evidence for event days. The utility of this database has previously been demonstrated in analyses of hospitalization and mortality. The database was created using open source software that works across operating systems. The prospect for future extensions to the database is enhanced by the description in this paper, and the availability of these data on the open access Github repository enables easy addition to the database with new data by the research community.

Sustainable fire management has eluded all industrial societies. Given the growing number and magnitude of wildfire events, prescribed fire is being increasingly promoted as the key to reducing wildfire risk. However, smoke from prescribed fires can adversely affect public health. We propose that the application of air quality standards can lead to the development and adoption of sustainable fire management approaches that lower the risk of economically and ecologically damaging wildfires while improving air quality and reducing climate-forcing emissions. For example, green fire breaks at the wildland–urban interface (WUI) can resist the spread of wildfires into urban areas. These could be created through mechanical thinning of trees, and then maintained by targeted prescribed fire to create biodiverse and aesthetically pleasing landscapes. The harvested woody debris could be used for pellets and other forms of bioenergy in residential space heating and electricity generation. Collectively, such an approach would reduce the negative health impacts of smoke pollution from wildfires, prescribed fires, and combustion of wood for domestic heating. We illustrate such possibilities by comparing current and potential fire management approaches in the temperate and environmentally similar landscapes of Vancouver Island in British Columbia, Canada and the island state of Tasmania in Australia.

Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO(2)) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R(2)=0.51) than in Seattle (R(2)=0.33). Scenario 2 produced improved R(2)-values in both cities (Victoria=0.58, Seattle=0.65), with further improvement achieved under Scenario 3 (Victoria=0.61, Seattle=0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.

Introduction: Land use regression (LUR) models based on measurement campaigns are frequently used to assess long-term exposure to ambient air pollution. Our aim was to develop LUR models based on f...