Grace Kuiper

Exposure to wildfire smoke is a well‐known concern for public health and is anticipated to worsen with an increase in wildfire activity related to climate change. This study uses satellite and ground‐based carbon monoxide (CO) measurements from 2004 to 2019 to evaluate a change in its seasonal cycle due to wildfire emissions. Monthly average CO total columns from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument over Alberta and Ontario, and from a ground‐based Fourier transform infrared spectrometer in downtown Toronto are compared before and after 1 January 2012, following previous literature. Between the two time periods, a new peak emerges in the seasonal cycle of CO, centered around August. Monthly emergency room admissions from Alberta and Ontario for nine cardiovascular and respiratory diseases are assessed with a difference in difference analysis, using MOPITT CO as the exposure metric. This analysis was used to calculate the change in monthly hospital admissions per 100,000 people, given a 1 ppb increase in XCO post‐2012 compared to pre‐2012, along with the 95% confidence interval (CI). For Ontario, this term is positive and significant for hypertension (change = 1.88, CI = 1.18–2.57), ischemic heart disease (0.50, CI = 0.12–0.88), arrhythmia (0.12, CI = 0.03–0.22), and asthma (0.31, CI = 0.05–0.57). For Alberta, there is a significant and positive interaction for arrhythmia (0.48, CI = 0.12–0.85). These results indicate that there was a statistically significant increase in adverse health outcomes for five of the eighteen disease‐province pairings associated with the increase in atmospheric CO after 2011 coinciding with enhanced wildfire emissions.

Reliable assessment of personal exposure to air pollution remains a challenge due to the limitations of monitoring technology. Recent technology developments, such as reductions in the size and cost of samplers as well as incorporation of continuous sensors for location, activity, and exposure (i.e., global positioning systems [GPS], accelerometers, and low-cost pollutant sensors), have advanced our ability to assess personal exposure to air pollution. This study evaluated the upgraded Ultrasonic Personal Aerosol Sampler (UPAS v2.1 PLUS) as a tool for quantifying time-integrated indoor and personal exposure to particulate matter (PM) and black carbon (BC) among a panel of participants in California's Central Valley and exploring personal exposures in different microenvironments using time/location-resolved PM data. Three field campaigns demonstrated that filter-derived PM₁₀, PM_2.5, PM₁₀ BC, and PM_2.5 BC concentrations measured using the UPAS were linear, unbiased, and precise compared to those measured using conventional personal sampling equipment. Time-resolved PM, GPS, and light intensity data from the UPAS allowed for personal PM_2.5 exposure assessment across microenvironments. The majority of daily PM_2.5 exposure occurred inside the home. Participants with higher out-of-home PM_2.5 exposures received those exposures primarily in agricultural and in-transit environments, in accordance with their self-reported occupational exposures. This study demonstrated the UPAS v2.1 PLUS is a reliable and valid tool for characterizing indoor air pollution and personal exposures in both temporal and spatial dimensions. Its enhanced capabilities should reduce the burden of personal activity logging in the field and enable accurate and precise estimation of exposures for epidemiological and community-based research.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (β: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (β:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Organophosphate (OP) pesticides are associated with numerous adverse health outcomes. Pesticide use data are available for California from the Pesticide Use Report (PUR), but household- and individual-level exposure factors have not been fully characterized to support its refinement as an exposure assessment tool. Unique exposure pathways, such as proximity to agricultural operations and direct occupational contact, further complicate pesticide exposure assessment among agricultural communities. We sought to identify influencing factors of pesticide exposure to support future exposure assessment and epidemiological studies. Household dust samples were collected from 28 homes in four California agricultural communities during January and June 2019 and were analyzed for the presence of OPs. Factors influencing household OPs were identified by a data-driven model via best subsets regression. Key factors that impacted dust OP levels included household cooling strategies, secondary occupational exposure to pesticides, and geographic location by community. Although PUR data demonstrate seasonal trends in pesticide application, this study did not identify season as an important factor, suggesting OP persistence in the home. These results will help refine pesticide exposure assessment for future studies and highlight important gaps in the literature, such as our understanding of pesticide degradation in an indoor environment.

This study explores methodologies for the data integration of antimicrobial use (AMU) and antimicrobial resistance (AMR) results within and across three food animal species, surveyed at the farm-level by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). The approach builds upon existing CIPARS methodology and principles from other AMU and AMR surveillance systems. Species level data integration involved: (1) standard CIPARS descriptive and temporal analysis of AMU/AMR, (2) synthesis of results, (3) selection of AMU and AMR outcomes for integration, (4) selection of candidate AMU indicators to enable comparisons of AMU levels between species and simultaneous assessment of AMU and AMR trends, (5) exploration of analytic options for studying associations between AMU and AMR, and (6) interpretation and visualization. The multi-species integration was also completed using the above approach. In addition, summarized reporting of internationally-recognized indicators of AMR (i.e., AMR adjusted for animal biomass) and AMU (mg/population correction unit, mg/kg animal biomass) is explored. It is envisaged that this approach for species and multi-species AMU–AMR data integration will be applied to the annual CIPARS farm-level data and progressively developed over time to inform AMU–AMR integrated surveillance best practices for further enhancement of AMU stewardship actions.

Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.