Long-term associations of sector-specific air pollution mixtures with mortality in Canada.

Previous studies often focus on individual air pollutants rather than their mixtures or specific sources. To assess associations between mixtures of fine particulate matter (PM), sulfur dioxide (SO), nitrogen dioxide (NO), and ozone (O) from various industrial and residential sources and mortality from ischemic heart disease (IHD), cardiovascular disease (CVD), and non-accidental causes. The 2006 Canadian Census Health and Environment Cohort (CanCHEC) was linked to the Canadian Vital Statistics Database included 3,019,125 adults with 36,112,640 person-years of follow-up.

Systemic Autoimmune Rheumatic Disease Risk: Association With Long-Term Exposure to Fine Particulate Matter.

Objective: Fine particulate matter (PM) is a possible trigger of systemic autoimmune rheumatic diseases (SARDs). We investigated SARDs risk related to long-term exposure to PM and its components (ammonium, black carbon, mineral dust, sea salt, nitrate, sulfate, organic matter), the composition of which may affect toxicity.

Methods: We assembled an open cohort of Quebec adults (without SARDs) using administrative health data from April 2000 to December 2019.

Fine Particulate Matter Components and Risk of Rheumatoid Arthritis: A Large General Canadian Open Cohort Study.

Objective: Exposure to fine particulate matter (PM) has been linked to many diseases. However, it remains unclear which PM chemical components for these diseases, including rheumatoid arthritis (RA), are more harmful. This study aimed to assess potential associations between PM components and RA and quantify the individual effects of each chemical component on RA risk.

The influence of urban trees and total vegetation on asthma development in children.

Objective: We aimed to assess whether the influence of urban vegetation on asthma development in children (<13 years) varies by type (e.g., total vegetation, tree type, and grass) and season.

Long-term exposure to fine particulate matter constituents and cognitive impairment among older adults: An 18-year Chinese nationwide cohort study.

Background: Although growing evidence has shown independent links of long-term exposure to fine particulate matter (PM) with cognitive impairment, the effects of its constituents remain unclear. This study aims to explore the associations of long-term exposure to ambient PM constituents' mixture with cognitive impairment in Chinese older adults, and to further identify the main contributor.

Methods: 15,274 adults ≥ 65 years old were recruited by the Chinese Longitudinal Healthy Longevity Study (CLHLS) and followed up through 7 waves during 2000-2018.

Systemic autoimmune rheumatic diseases and multiple industrial air pollutant emissions: A large general population Canadian cohort analysis.

Background: Past investigations of air pollution and systemic autoimmune rheumatic diseases (SARDs) typically focused on individual (not mixed) and overall environmental emissions. We assessed mixtures of industrial emissions of fine particulate matter (PM), nitrogen dioxide (NO), and sulfur dioxide (SO) and SARDs onset in Ontario, Canada.

Methods: We assembled an open cohort of over 12 million adults (without SARD diagnoses at cohort entry) based on provincial health data for 2007-2020 and followed them until SARD onset, death, emigration, or end of study (December 2020).

Long-term exposure to fine particulate matter and ozone and the onset of systemic autoimmune rheumatic diseases: an open cohort study in Quebec, Canada.

Objectives: To estimate associations between fine particulate matter (PM) and ozone and the onset of systemic autoimmune rheumatic diseases (SARDs).

Methods: An open cohort of over 6 million adults was constructed from provincial physician billing and hospitalization records between 2000 and 2013. We defined incident SARD cases (SLE, Sjogren's syndrome, scleroderma, polymyositis, dermatomyositis, polyarteritis nodosa and related conditions, polymyalgia rheumatic, other necrotizing vasculopathies, and undifferentiated connective tissue disease) based on at least two relevant billing diagnostic codes (within 2 years, with at least 1 billing from a rheumatologist), or at least one relevant hospitalization diagnostic code.

Fine particulate matter components and interstitial lung disease in rheumatoid arthritis.

Background: Exposure to ambient fine particulate matter with an aerodynamic diameter <2.5 μg·m (PM) is a risk factor for pulmonary and systemic autoimmune diseases; however, evidence on which PM chemical components are more harmful is still scant. Our goal is to investigate potential associations between major PM components and interstitial lung disease (ILD) onset in rheumatoid arthritis (RA).

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.

Long-term ozone exposure and mortality from neurological diseases in Canada.

Background: There is increasing interest in the health effects of air pollution. However, the relationships between ozone exposure and mortality attributable to neurological diseases remain unclear.

Objectives: To assess associations of long-term exposure to ozone with death from Parkinson's disease, dementia, stroke, and multiple sclerosis.

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models.

Sunlight Exposure, Sun-Protective Behavior, and Anti-Citrullinated Protein Antibody Positivity: A General Population-Based Study in Quebec, Canada.

Objective: To examine associations between sunlight exposure and anti-citrullinated protein antibodies (ACPAs) using general population data in Quebec, Canada.

Methods: A random sample of 7,600 individuals (including 786 subjects who were ACPA positive and 201 self-reported rheumatoid arthritis [RA] cases) from the CARTaGENE cohort was studied cross-sectionally. All subjects were nested in 4 census metropolitan areas, and mixed-effects logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for ACPA positivity related to sunlight exposure, adjusting for sun-block use, industrial fine particulate matter (PM ) exposures, smoking, age, sex, French Canadian ancestry, and family income.

Long-term exposure to a mixture of industrial SO, NO, and PM and anti-citrullinated protein antibody positivity.

Background: Studies of associations between industrial air emissions and rheumatic diseases, or diseases-related serological biomarkers, are few. Moreover, previous evaluations typically studied individual (not mixed) emissions. We investigated associations between individual and combined exposures to industrial sulfur dioxide (SO), nitrogen dioxide (NO), and fine particles matter (PM) on anti-citrullinated protein antibodies (ACPA), a characteristic biomarker for rheumatoid arthritis (RA).

Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data.

Rapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking.

Tracking the origin of early COVID-19 cases in Canada.

The original coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China has become a global pandemic. By tracking the earliest 118 COVID-19 cases in Canada, we produced a Voronoi treemap to show the travel origins of the country's earliest COVID-19 cases. By March 11, 2020, even though the majority (64.

Investigating associations between anti-nuclear antibody positivity and combined long-term exposures to NO, O, and PM using a Bayesian kernel machine regression approach.

Background: Air pollution has many adverse health effects, but the combined or synergistic effects of multiple ambient air pollutants on anti-nuclear antibodies (ANA, a serologic marker of systemic autoimmune rheumatic disease, SARDs) have never been assessed.

Objective: To flexibly model ANA and individual and joint associations of long-term exposures to nitrogen dioxide (NO), ozone (O), and fine particles matter (PM) using a Bayesian Kernel machine regression (BKMR) approach and to compare the results to those from individual logistic regressions.

Methods: Serum ANA positivity was determined for randomly selected CARTaGENE general population subjects in Quebec, Canada.

Revisiting the estimations of PM-attributable mortality with advancements in PM mapping and mortality statistics.

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM) and mortality statistics are increasingly used to examine the health effects of PM. Choices of these datasets with difference geographic characteristics (e.g.

Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model.

Remote sensing image products (e.g. brightness of nighttime lights and land cover/land use types) have been widely used to disaggregate census data to produce gridded population maps for large geographic areas.

Improve ground-level PM concentration mapping using a random forests-based geostatistical approach.

Accurate measurements of ground-level PM (particulate matter with aerodynamic diameters equal to or less than 2.5 μm) concentrations are critically important to human and environmental health studies. In this regard, satellite-derived gridded PM datasets, particularly those datasets derived from chemical transport models (CTM), have demonstrated unique attractiveness in terms of their geographic and temporal coverage.

The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance.

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance--particularly the emergence of digital epidemiology--provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons.

[Study on the main influencing factors of mixed-pixel spectral characteristics].

Hyperspectral remote sensing can improve the identification and classification of surface features through the spectrum comparing and matching to achieve classification and recognition. Because of the spatial resolution of the sensor as well as the difference in complexity and diversity on the ground, mixed pixels in the image are prevalent in remote sensing. The problem of subpixel unmixing is a prominent issue in the quantitative application of remote sensing.

[Effects of multi-angle hyperspectral polarized reflection by forest soil].

In the present study, the authors measured samples of typical forest soils in different states with multi-angle hyperspectral polarized reflections. The authors analyzed multi-angle hyperspectral polarized reflections of soil data with various viewing zenith angles, incidence angles, relative azimuth angles, polarized states, soil water content and soil granule. The authors found that those factors affected the reflectance values of forest soils but not the spectral feature.