WHO air quality database: relevance, history and future developments.

Air pollution is the second most important risk factor for noncommunicable diseases, but air quality monitoring is lacking in many low- and middle-income countries. The World Health Organization (WHO) recently released its 2022 updated air quality database status report. This report contains data from about 6743 human settlements, a sixfold increase from 1102 settlements in its first publication in 2011, which shows that air pollution is increasingly recognized as a health priority at global and national levels. However, progress varies across the world. More than 90% of the settlements in the database are in high- and middle-income countries and areas mainly in China, Europe, India and North America. The database is crucial for increasing awareness of air pollution, and for calculating global exposures and the corresponding burden of disease attributable to air pollution. This article describes the progress made and challenges in collecting air quality data. The database uses official data sources which can be difficult to access and assess, because air quality monitoring is done by different government bodies or uses varying monitoring methods. These air quality data can be used by the health sector to engage in discussions on monitoring air quality to protect public health, and facilitate multisectoral engagement of United Nations agencies to support countries to conform with the 2021 WHO air quality guidelines. Although air pollution levels in most countries are higher than those recommended in the guidelines, any action policy-makers take to reduce air pollution will help reduce the burden of air pollution on health.

Bien que la pollution de l'air représente le deuxième facteur de risque le plus important pour les maladies non transmissibles, de nombreux pays à revenu faible et intermédiaire ne mènent aucun contrôle de la qualité de l'air. L'Organisation mondiale de la Santé (OMS) a récemment publié l'édition 2022 du rapport de situation relatif à sa base de données sur la qualité de l'air. Ce rapport renferme des informations sur près de 6743 établissements humains, un chiffre six fois supérieur aux 1102 établissements humains figurant dans la première publication de 2011, ce qui montre que la pollution de l'air est davantage reconnue comme une priorité en matière de santé, tant à l'échelle nationale qu'internationale. Pourtant, les avancées ne sont pas les mêmes partout dans le monde. Plus de 90% des établissements mentionnés dans la base de données se trouvent dans des pays à revenu faible et intermédiaire, ainsi que dans des régions principalement situées en Chine, en Europe, en Inde et en Amérique du Nord. Cette base de données est essentielle pour mieux sensibiliser à la pollution de l'air, mais aussi pour calculer l'exposition mondiale et l'impact des maladies qui lui sont attribuables. Le présent article décrit les progrès réalisés et les défis qui subsistent dans la collecte d'informations liées à la qualité de l'air. La base de données utilise des sources officielles, qui peuvent être difficiles d'accès et compliquées à évaluer car le contrôle de la qualité de l'air est effectué par plusieurs organismes gouvernementaux ou emploie des méthodes différentes. Les informations ainsi récoltées peuvent être exploitées par le secteur de la santé pour entamer des discussions sur le contrôle de la qualité de l'air. Objectif: préserver la santé publique et favoriser la mobilisation multisectorielle d'agences des Nations Unies pour aider les pays à se conformer aux lignes directrices de l'OMS relatives à la qualité de l'air, qui datent de 2021. Même si, dans la plupart des pays, les niveaux de pollution de l'air dépassent les recommandations formulées dans ces lignes directrices, toute action entreprise par les responsables politiques pour les faire baisser contribuera à réduire l'impact qu'exerce cette pollution sur la santé.

La contaminación del aire es el segundo factor de riesgo más importante de las enfermedades no transmisibles, pero en muchos países de ingresos bajos y medios no se vigila la calidad del aire. La Organización Mundial de la Salud (OMS) publicó hace poco su informe actualizado de 2022 sobre el estado de la base de datos de calidad del aire. Este informe contiene datos de unos 6743 asentamientos humanos, es decir, seis veces más que los 1102 asentamientos de su primera publicación en 2011, lo que demuestra que la contaminación del aire se reconoce cada vez más como una prioridad sanitaria a nivel mundial y nacional. Sin embargo, los progresos varían en todo el mundo. Más del 90% de los asentamientos de la base de datos se encuentran en países y regiones de ingresos altos y medios, principalmente en China, Europa, India y Norteamérica. La base de datos es esencial para aumentar la concienciación sobre la contaminación del aire y para calcular las exposiciones globales y la correspondiente carga de morbilidad atribuible a la contaminación del aire. Este artículo describe los progresos realizados y los desafíos que plantea la recopilación de datos sobre la calidad del aire. La base de datos utiliza fuentes de datos oficiales a las que puede resultar difícil acceder y evaluar porque el control de la calidad del aire lo realizan diferentes organismos gubernamentales o utilizan métodos de control que varían. El sector sanitario puede utilizar estos datos sobre la calidad del aire para participar en debates sobre la vigilancia de la calidad del aire con el fin de proteger la salud pública y facilitar el compromiso multisectorial de los organismos de las Naciones Unidas para ayudar a los países a cumplir las directrices de la OMS 2021 sobre la calidad del aire. Aunque los niveles de contaminación del aire en la mayoría de los países son superiores a los recomendados en las directrices, cualquier medida que adopten los responsables de formular políticas para reducir la contaminación del aire contribuirá a reducir la carga de la contaminación del aire sobre la salud.

Characterizing long-term NO2 concentration surfaces across a large metropolitan area through spatiotemporal land use regression modelling of mobile measurements.

A spatiotemporal land use regression (LUR) model optimized to predict nitrogen dioxide (NO2) concentrations obtained from on-road, mobile measurements collected in 2015-16 was independently evaluated using concentrations observed at multiple sites across Toronto, Canada, obtained more than ten years earlier. This spatiotemporal LUR modelling approach improves upon estimates of historical NO2 concentrations derived from the previously used method of back-extrapolation. The optimal spatiotemporal LUR model (R2 = 0.71 for prediction of NO2 data in 2002 and 2004) uses daily average NO2 concentrations observed at multiple long-term monitoring sites and hourly average wind speed recorded at a single site, along with spatial predictors based on geographical information system data, to estimate NO2 levels for time periods outside of those used for model development. While the model tended to underestimate samplers located close to the roadway, it showed great accuracy when estimating samplers located beyond 100 m which are probably more relevant for exposure at residences. This study shows that spatiotemporal LUR models developed from strategic, multi-day (30 days in 3 different months) mobile measurements can enhance LUR model's ability to estimate long-term, intra-urban NO2 patterns. Furthermore, the mobile sampling strategy enabled this new LUR model to cover a larger domain of Toronto and outlying suburban communities, thereby increasing the potential population for future epidemiological studies.

Healthy built environment: Spatial patterns and relationships of multiple exposures and deprivation in Toronto, Montreal and Vancouver.

BACKGROUND: Various aspects of the urban environment and neighbourhood socio-economic status interact with each other to affect health. Few studies to date have quantitatively assessed intersections of multiple urban environmental factors and their distribution across levels of deprivation. OBJECTIVES: To explore the spatial patterns of urban environmental exposures within three large Canadian cities, assess how exposures are distributed across socio-economic deprivation gradients, and identify clusters of favourable or unfavourable environmental characteristics. METHODS: We indexed nationally standardized estimates of active living friendliness (i.e. "walkability"), NO2 air pollution, and greenness to 6-digit postal codes within the cities of Toronto, Montreal and Vancouver. We compared the distribution of within-city exposure tertiles across quintiles of material deprivation. Tertiles of each exposure were then overlaid with each other in order to identify potentially favorable (high walkability, low NO2, high greenness) and unfavorable (low walkability, high NO2, and low greenness) environments. RESULTS: In all three cities, high walkability was more common in least deprived areas and less prevalent in highly deprived areas. We also generally saw a greater prevalence of postal codes with high vegetation indices and low NO2 in areas with low deprivation, and a lower greenness prevalence and higher NO2 concentrations in highly deprived areas, suggesting environmental inequity is occurring. Our study showed that relatively few postal codes were simultaneously characterized by desirable or undesirable walkability, NO2and greenness tertiles. DISCUSSION: Spatial analyses of multiple standardized urban environmental factors such as the ones presented in this manuscript can help refine municipal investments and policy priorities. This study illustrates a methodology to prioritize areas for interventions that increase active living and exposure to urban vegetation, as well as lower air pollution. Our results also highlight the importance of considering the intersections between the built environment and socio-economic status in city planning and urban public health decision-making.

Transboundary and traffic influences on air pollution across two Caribbean islands.

Exposure to ambient air pollution has been linked to adverse health outcomes ranging from asthma to premature mortality. However, little to no information exists on the exposure of residents and visitors in the Caribbean islands. While a few previous studies have quantified levels of PM10 (particulate matter <10 µm) from Sahara dust in Trinidad, our study focussed on a local source of air pollution, traffic emissions. Mass concentrations of black carbon (BC) and PM2.5 (PM <2.5 µm) were measured at ten locations across the islands of Trinidad and Tobago over a three-week period. PM2.5 concentrations were observed to be heavily influenced by air masses showing origins from the Sahara Desert (31%), North America (26%) and Atlantic Ocean (42%), which resulted in similar average concentrations between the two islands. Average concentrations of BC were five times higher in Trinidad than Tobago (2.0 vs 0.43 µg/m3). In addition, BC in Trinidad was three times higher near than away from major roads (2.21 vs. 0.72 µg/m3), with concentrations reaching levels comparable to those near highways in large Metropolitan cities. The elevated BC concentrations observed in this study suggests that significant exposure to diesel exhaust is occurring in Trinidad, with significant contributions from traffic.

Near-Road Air Pollutant Measurements: Accounting for Inter-Site Variability Using Emission Factors.

A daily integrated emission factor (EF) method was applied to data from three near-road monitoring sites to identify variables that impact traffic related pollutant concentrations in the near-road environment. The sites were operated for 20 months in 2015-2017, with each site differing in terms of design, local meteorology, and fleet compositions. Measurement distance from the roadway and local meteorology were found to affect pollutant concentrations irrespective of background subtraction. However, using emission factors mostly accounted for the effects of dilution and dispersion, allowing intersite differences in emissions to be resolved. A multiple linear regression model that included predictor variables such as fraction of larger vehicles (>7.6 m in length; i.e., heavy-duty vehicles), vehicle speed, and ambient temperature accounted for intersite variability of the fleet average NO, NO x, and particle number EFs (R2:0.50-0.75), with lower model performance for CO and black carbon (BC) EFs (R2:0.28-0.46). NO x and BC EFs were affected more than CO and particle number EFs by the fraction of larger vehicles, which also resulted in measurable weekday/weekend differences. Pollutant EFs also varied with ambient temperature and because there were little seasonal changes in fleet composition, this was attributed to changes in fuel composition and/or post-tailpipe transformation of pollutants.

Underestimation of sulfate concentration in PM2.5 using a semi-continuous particle instrument based on ion chromatography.

Recently, a variety of ion-chromatography-based semi-continuous particle instruments such as the Dionex gas particle ion chromatograph (GP-IC), wet-annular denuder/steam-jet aerosol collector (WAD/SJAC), particle-into-liquid sampler with ion chromatograph (PILS-IC), gas and aerosol monitoring system (GAMS) have been introduced for measuring particle chemical components in the atmosphere. It has been reported that sulfate concentrations in PM2.5 measured by these semi-continuous particle instruments correlate well with those measured by other semi-continuous instruments such as the Aerodyne aerosol mass spectrometer (AMS), R&P 8400S and R&P 8400N analyzers, and Thermo model 5020 sulfate particle analyzer and at times exhibit a unity slope. However, the sulfate concentration measured by some of these semi-continuous instruments has been reported to be underestimated by 20-50%, as compared to that in PM2.5 filter samples. In this study, numerous potential causes for underestimation of the sulfate concentrations by the GP-IC were investigated. We found a 30-40% negative artifact arising from an improper calibration procedure inherent within the instrument design. An improved calibration procedure was developed and it substantially reduced the sulfate concentration difference between the GP-IC measurements and PM2.5 filter samples from 36% to 5%.