A scoping review of human health co-benefits of forest-based climate change mitigation in Europe.

Climate change is a pressing global challenge with profound implications for human health. Forest-based climate change mitigation strategies, such as afforestation, reforestation, and sustainable forest management, offer promising solutions to mitigate climate change and simultaneously yield substantial co-benefits for human health. The objective of this scoping review was to examine research trends related to the interdisciplinary nexus between forests as carbon sinks and human health co-benefits. We developed a conceptual framework model, supporting the inclusion of exposure pathways, such as recreational opportunities or aesthetic experiences, in the co-benefit context. We used a scoping review methodology to identify the proportion of European research on forest-based mitigation strategies that acknowledge the interconnection between mitigation strategies and human impacts. We also aimed to assess whether synergies and trade-offs between forest-based carbon sink capacity and human co-benefits has been analysed and quantified. From the initial 4,062 records retrieved, 349 reports analysed European forest management principles and factors related to climate change mitigation capacity. Of those, 97 studies acknowledged human co-benefits and 13 studies quantified the impacts on exposure pathways or health co-benefits and were included for full review. Our analysis demonstrates that there is potential for synergies related to optimising carbon sink capacity together with human co-benefits, but there is currently a lack of holistic research approaches assessing these interrelationships. We suggest enhanced interdisciplinary efforts, using for example multideterminant modelling approaches, to advance evidence and understanding of the forest and health nexus in the context of climate change mitigation.

The Superblock model: A review of an innovative urban model for sustainability, liveability, health and well-being.

INTRODUCTION: Current urban and transport planning practices have significant negative health, environmental, social and economic impacts in most cities. New urban development models and policies are needed to reduce these negative impacts. The Superblock model is one such innovative urban model that can significantly reduce these negative impacts through reshaping public spaces into more diverse uses such as increase in green space, infrastructure supporting social contacts and physical activity, and through prioritization of active mobility and public transport, thereby reducing air pollution, noise and urban heat island effects. This paper reviews key aspects of the Superblock model, its implementation and initial evaluations in Barcelona and the potential international uptake of the model in Europe and globally, focusing on environmental, climate, lifestyle, liveability and health aspects. METHODS: We used a narrative meta-review approach and PubMed and Google scholar databases were searched using specific terms. RESULTS: The implementation of the Super block model in Barcelona is slow, but with initial improvement in, for example, environmental, lifestyle, liveability and health indicators, although not so consistently. When applied on a large scale, the implementation of the Superblock model is not only likely to result in better environmental conditions, health and wellbeing, but can also contribute to the fight against the climate crisis. There is a need for further expansion of the program and further evaluation of its impacts and answers to related concerns, such as environmental equity and gentrification, traffic and related environmental exposure displacement. The implementation of the Superblock model gained a growing international reputation and variations of it are being planned or implemented in cities worldwide. Initial modelling exercises showed that it could be implemented in large parts of many cities. CONCLUSION: The Superblock model is an innovative urban model that addresses environmental, climate, liveability and health concerns in cities. Adapted versions of the Barcelona Superblock model are being implemented in cities around Europe and further implementation, monitoring and evaluation are encouraged. The Superblock model can be considered an important public health intervention that will reduce mortality and morbidity and generate cost savings for health and other sectors.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study.

BACKGROUND: A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19. METHODS: The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days. RESULTS: Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant. CONCLUSIONS: Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Who is more vulnerable to effects of long-term exposure to air pollution on COVID-19 hospitalisation?

OBJECTIVE: Factors that shape individuals' vulnerability to the effects of air pollution on COVID-19 severity remain poorly understood. We evaluated whether the association between long-term exposure to ambient NO2, PM2.5, and PM10 and COVID-19 hospitalisation differs by age, sex, individual income, area-level socioeconomic status, arterial hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. METHODS: We analysed a population-based cohort of 4,639,184 adults in Catalonia, Spain, during 2020. We fitted Cox proportional hazard models adjusted for several potential confounding factors and evaluated the interaction effect between vulnerability indicators and the 2019 annual average of NO2, PM2.5, and PM10. We evaluated interaction on both additive and multiplicative scales. RESULTS: Overall, the association was additive between air pollution and the vulnerable groups. Air pollution and vulnerability indicators had a synergistic (greater than additive) effect for males and individuals with low income or living in the most deprived neighbourhoods. The Relative Excess Risk due to Interaction (RERI) was 0.21, 95 % CI, 0.15 to 0.27 for NO2 and 0.16, 95 % CI, 0.11 to 0.22 for PM2.5 for males; 0.13, 95 % CI, 0.09 to 0.18 for NO2 and 0.10, 95 % CI, 0.05 to 0.14 for PM2.5 for lower individual income and 0.17, 95 % CI, 0.12 to 0.22 for NO2 and 0.09, 95 % CI, 0.05 to 0.14 for PM2.5 for lower area-level socioeconomic status. Results for PM10 were similar to PM2.5. Results on multiplicative scale were inconsistent. CONCLUSIONS: Long-term exposure to air pollution had a larger synergistic effect on COVID-19 hospitalisation for males and those with lower individual- and area-level socioeconomic status.

Associations between landscape fires and child morbidity in southern Mozambique: a time-series study.

BACKGROUND: Epidemiological evidence linking exposure to landscape fires to child health remains scarce. We assessed the association between daily landscape fire smoke and child hospital visits and admissions in the Manhiça district, Mozambique, an area characterised by frequent forest and cropland fires. METHODS: In this time-series analysis (2012-20), our primary metric for exposure to landscape fires was fire-originated PM2·5 from smoke dispersion hindcasts. We also assessed total and upwind fire exposure using daily satellite-derived fire density data. Daily numbers of hospital visits and admissions were extracted from an ongoing paediatric morbidity surveillance system (children aged ≤15 years). We applied quasi-Poisson regression models controlling for season, long-term trend, day of the week, temperature, and rainfall, and offsetting by annual population-time at risk to examine lag-specific association of fires on morbidity. FINDINGS: A 10 µg/m3 increase in fire-originated PM2·5 was associated with a 6·12% (95% CI 0·37-12·21) increase in all-cause and a 12·43% (5·07-20·31) increase in respiratory-linked hospital visits on the following day. Positive associations were also observed for lag 0 and the cumulative lag of 0-1 days. Null associations were observed for hospital admissions. Landscape fires mostly occurred in forested areas; however, associations with child morbidity were stronger for cropland than for forest fires. INTERPRETATION: Landscape fire smoke was associated with all-cause and respiratory-linked morbidity in children. Improved exposure assessment is needed to better quantify the contribution of landscape fire smoke to child health in regions with scarce air pollution monitoring. FUNDING: H2020 project EXHAUSTION, Academy of Finland, Spanish Ministry of Science and Innovation, Generalitat de Catalunya, and Government of Mozambique and Spanish Agency for International Cooperation and Development.

Associations between landscape fires and child morbidity in southern Mozambique: a time-series study

Background: Epidemiological evidence linking exposure to landscape fires to child health remains scarce. We assessed the association between daily landscape fire smoke and child hospital visits and admissions in the Manhiça district, Mozambique, an area characterised by frequent forest and cropland fires. Methods: In this time-series analysis (2012-20), our primary metric for exposure to landscape fires was fire-originated PM2·5 from smoke dispersion hindcasts. We also assessed total and upwind fire exposure using daily satellite-derived fire density data. Daily numbers of hospital visits and admissions were extracted from an ongoing paediatric morbidity surveillance system (children aged ≤15 years). We applied quasi-Poisson regression models controlling for season, long-term trend, day of the week, temperature, and rainfall, and offsetting by annual population-time at risk to examine lag-specific association of fires on morbidity. Findings: A 10 µg/m3 increase in fire-originated PM2·5 was associated with a 6·12% (95% CI 0·37-12·21) increase in all-cause and a 12·43% (5·07-20·31) increase in respiratory-linked hospital visits on the following day. Positive associations were also observed for lag 0 and the cumulative lag of 0-1 days. Null associations were observed for hospital admissions. Landscape fires mostly occurred in forested areas; however, associations with child morbidity were stronger for cropland than for forest fires. Interpretation: Landscape fire smoke was associated with all-cause and respiratory-linked morbidity in children. Improved exposure assessment is needed to better quantify the contribution of landscape fire smoke to child health in regions with scarce air pollution monitoring. Funding: H2020 project EXHAUSTION, Academy of Finland, Spanish Ministry of Science and Innovation, Generalitat de Catalunya, and Government of Mozambique and Spanish Agency for International Cooperation and Development.

The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study.

Background: Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date. Methods: We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation. Findings: We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths. Interpretation: The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making. Funding: The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Residential relocation to assess impact of changes in the living environment on cardio-respiratory health: A narrative literature review with considerations for exposome research.

Residential relocation studies have become increasingly valuable tools for evaluating the effects of changing living environments on human health, but little is known about their application to multiple aspects of the living environment and the most appropriate methodology. This narrative review explores the utility of residential relocation as a natural experiment for studying the impact of changing urban exposures on cardio-metabolic health in high-income settings. It provides a comprehensive overview of the use of residential relocation studies, evaluates their methodological approaches, and synthesizes findings related to health behaviors and cardio-metabolic outcomes. Our search identified 43 relevant studies published between January 1995 and February 2023, from eight countries, predominantly the USA, Canada, and Australia. The majority of eligible studies were published between 2012 and 2021 and examined changes in various domains of the living environment, such as walkability, the built and social environments, but rarely combinations of exposures. Included studies displayed heterogeneity in design and outcomes, 25 involving only movers and 18 considering both movers and non-movers. To mitigate the issue of residential self-selection bias, most studies employed a "change-in-change" design and adjusted for baseline covariates but only a fraction of them accounted for time-varying confounding. Relocation causes simultaneous changes in various features of the living environment, which presents an opportunity for exposome research to establish causal relationships, using large datasets with increased statistical power and a wide range of health outcomes, behaviors and biomarkers. Residential relocation is not a random process. Thus, studies focusing on living environment characteristics need to carefully select time-varying covariates and reference group. Overall, this review informs future research by guiding choices in study design, data requirements, and statistical methodologies. Ultimately, it contributes to the advancement of the urban exposome field and enhances our understanding of the complex relationship between urban environments and human health.

Air pollution deaths attributable to fossil fuels: observational and modelling study.

OBJECTIVES: To estimate all cause and cause specific deaths that are attributable to fossil fuel related air pollution and to assess potential health benefits from policies that replace fossil fuels with clean, renewable energy sources. DESIGN: Observational and modelling study. METHODS: An updated atmospheric composition model, a newly developed relative risk model, and satellite based data were used to determine exposure to ambient air pollution, estimate all cause and disease specific mortality, and attribute them to emission categories. DATA SOURCES: Data from the global burden of disease 2019 study, observational fine particulate matter and population data from National Aeronautics and Space Administration (NASA) satellites, and atmospheric chemistry, aerosol, and relative risk modelling for 2019. RESULTS: Globally, all cause excess deaths due to fine particulate and ozone air pollution are estimated at 8.34 million (95% confidence interval 5.63 to 11.19) deaths per year. Most (52%) of the mortality burden is related to cardiometabolic conditions, particularly ischaemic heart disease (30%). Stroke and chronic obstructive pulmonary disease both account for 16% of mortality burden. About 20% of all cause mortality is undefined, with arterial hypertension and neurodegenerative diseases possibly implicated. An estimated 5.13 million (3.63 to 6.32) excess deaths per year globally are attributable to ambient air pollution from fossil fuel use and therefore could potentially be avoided by phasing out fossil fuels. This figure corresponds to 82% of the maximum number of air pollution deaths that could be averted by controlling all anthropogenic emissions. Smaller reductions, rather than a complete phase-out, indicate that the responses are not strongly non-linear. Reductions in emission related to fossil fuels at all levels of air pollution can decrease the number of attributable deaths substantially. Estimates of avoidable excess deaths are markedly higher in this study than most previous studies for these reasons: the new relative risk model has implications for high income (largely fossil fuel intensive) countries and for low and middle income countries where the use of fossil fuels is increasing; this study accounts for all cause mortality in addition to disease specific mortality; and the large reduction in air pollution from a fossil fuel phase-out can greatly reduce exposure. CONCLUSION: Phasing out fossil fuels is deemed to be an effective intervention to improve health and save lives as part the United Nations' goal of climate neutrality by 2050. Ambient air pollution would no longer be a leading, environmental health risk factor if the use of fossil fuels were superseded by equitable access to clean sources of renewable energy.

Estimating daily air temperature and pollution in Catalonia: A comprehensive spatiotemporal modelling of multiple exposures.

Environmental epidemiology studies require models of multiple exposures to adjust for co-exposure and explore interactions. We estimated spatiotemporal exposure to surface air temperature and pollution (PM2.5, PM10, NO2, O3) at high spatiotemporal resolution (daily, 250 m) for 2018-2020 in Catalonia. Innovations include the use of TROPOMI products, a data split for remote sensing gap-filling evaluation, estimation of prediction uncertainty, and use of explainable machine learning. We compiled meteorological and air quality station measurements, climate and atmospheric composition reanalyses, remote sensing products, and other spatiotemporal data. We performed gap-filling of remotely-sensed products using Random Forest (RF) models and validated them using Out-Of-Bag (OOB) samples and a structured data split. The exposure modelling workflow consisted of: 1) PM2.5 station imputation with PM10 data; 2) quantile RF (QRF) model fitting; and 3) geostatistical residual spatial interpolation. Prediction uncertainty was estimated using QRF. SHAP values were used to examine variable importance and the fitted relationships. Model performance was assessed via nested CV at the station level. Evaluation of the gap-filling models using the structured split showed error underestimation when using OOB. Temperature models had the best performance (R2 =0.98) followed by the gaseous air pollutants (R2 =0.81 for NO2 and 0.86 for O3), while the performance of the PM2.5 and PM10 models was lower (R2 =0.57 and 0.63 respectively). Predicted exposure patterns captured urban heat island effects, dust advection events, and NO2 hotspots. SHAP values estimated a high importance of TROPOMI tropospheric NO2 columns in PM and NO2 models, and confirmed that the fitted associations conformed to prior knowledge. Our work highlights the importance of correctly validating gap-filling models and the potential of TROPOMI measurements. Moderate performance in PM models can be partly explained by the poor station coverage. Our exposure estimates can be used in epidemiological studies potentially accounting for exposure uncertainty.

Mixtures of long-term exposure to ambient air pollution, built environment and temperature and stroke incidence across Europe.

INTRODUCTION: The complex interplay of multiple environmental factors and cardiovascular has scarcely been studied. Within the EXPANSE project, we evaluated the association between long-term exposure to multiple environmental indices and stroke incidence across Europe. METHODS: Participants from three traditional adult cohorts (Germany, Netherlands and Sweden) and four administrative cohorts (Catalonia [region Spain], Rome [city-wide], Greece and Sweden [nationwide]) were followed until incident stroke, death, migration, loss of follow-up or study end. We estimated exposures at residential addresses from different exposure domains: air pollution (nitrogen dioxide (NO2), particulate matter < 2.5 µm (PM2.5), black carbon (BC), ozone), built environment (green/blue spaces, impervious surfaces) and meteorology (seasonal mean and standard deviation of temperatures). Associations between environmental exposures and stroke were estimated in single and multiple-exposure Cox proportional hazard models, and Principal Component (PC) Analyses derived prototypes for specific exposures domains. We carried out random effects meta-analyses by cohort type. RESULTS: In over 15 million participants, increased levels of NO2 and BC were associated with increased higher stroke incidence in both cohort types. Increased Normalized Difference Vegetation Index (NDVI) was associated with a lower stroke incidence in both cohort types, whereas an increase in impervious surface was associated with an increase in stroke incidence. The first PC of the air pollution domain (PM2.5, NO2 and BC) was associated with an increase in stroke incidence. For the built environment, higher levels of NDVI and lower levels of impervious surfaces were associated with a protective effect [%change in HR per 1 unit = -2.0 (95 %CI, -5.9;2.0) and -1.1(95 %CI, -2.0; -0.3) for traditional adult and administrative cohorts, respectively]. No clear patterns were observed for distance to blue spaces or temperature parameters. CONCLUSIONS: We observed increased HRs for stroke with exposure to PM2.5, NO2 and BC, lower levels of greenness and higher impervious surface in single and combined exposure models.

Model Choice for Quantitative Health Impact Assessment and Modelling: An Expert Consultation and Narrative Literature Review.

BACKGROUND: Health impact assessment (HIA) is a widely used process that aims to identify the health impacts, positive or negative, of a policy or intervention that is not necessarily placed in the health sector. Most HIAs are done prospectively and aim to forecast expected health impacts under assumed policy implementation. HIAs may quantitatively and/ or qualitatively assess health impacts, with this study focusing on the former. A variety of quantitative modelling methods exist that are used for forecasting health impacts, however, they differ in application area, data requirements, assumptions, risk modelling, complexities, limitations, strengths, and comprehensibility. We reviewed relevant models, so as to provide public health researchers with considerations for HIA model choice. METHODS: Based on an HIA expert consultation, combined with a narrative literature review, we identified the most relevant models that can be used for health impact forecasting. We narratively and comparatively reviewed the models, according to their fields of application, their configuration and purposes, counterfactual scenarios, underlying assumptions, health risk modelling, limitations and strengths. RESULTS: Seven relevant models for health impacts forecasting were identified, consisting of (i) comparative risk assessment (CRA), (ii) time series analysis (TSA), (iii) compartmental models (CMs), (iv) structural models (SMs), (v) agent-based models (ABMs), (vi) microsimulations (MS), and (vii) artificial intelligence (AI)/machine learning (ML). These models represent a variety in approaches and vary in the fields of HIA application, complexity and comprehensibility. We provide a set of criteria for HIA model choice. Researchers must consider that model input assumptions match the available data and parameter structures, the available resources, and that model outputs match the research question, meet expectations and are comprehensible to end-users. CONCLUSION: The reviewed models have specific characteristics, related to available data and parameter structures, computational implementation, interpretation and comprehensibility, which the researcher should critically consider before HIA model choice.

Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment.

BACKGROUND: Ambient air pollution is a major risk to health and wellbeing in European cities. We aimed to estimate spatial and sector-specific contributions of emissions to ambient air pollution and evaluate the effects of source-specific reductions in pollutants on mortality in European cities to support targeted source-specific actions to address air pollution and promote population health. METHODS: We conducted a health impact assessment of data from 2015 for 857 European cities to estimate source contributions to annual PM2·5 and NO2 concentrations using the Screening for High Emission Reduction Potentials for Air quality tool. We evaluated contributions from transport, industry, energy, residential, agriculture, shipping, and aviation, other, natural, and external sources. For each city and sector, three spatial levels were considered: contributions from the same city, the rest of the country, and transboundary. Mortality effects were estimated for adult populations (ie, ≥20 years) following standard comparative risk assessment methods to calculate the annual mortality preventable on spatial and sector-specific reductions in PM2·5 and NO2. FINDINGS: We observed strong variability in spatial and sectoral contributions among European cities. For PM2·5, the main contributors to mortality were the residential (mean contribution of 22·7% [SD 10·2]) and agricultural (18·0% [7·7]) sectors, followed by industry (13·8% [6·0]), transport (13·5% [5·8]), energy (10·0% [6·4]), and shipping (5·5% [5·7]). For NO2, the main contributor to mortality was transport (48·5% [SD 15·2]), with additional contributions from industry (15·0% [10·8]), energy (14·7% [12·9]), residential (10·3% [5·0]), and shipping (9·7% [12·7]). The mean city contribution to its own air pollution mortality was 13·5% (SD 9·9) for PM2·5 and 34·4% (19·6) for NO2, and contribution increased among cities of largest area (22·3% [12·2] for PM2·5 and 52·2% [19·4] for NO2) and among European capitals (29·9% [12·5] for PM2·5 and 62·7% [14·7] for NO2). INTERPRETATION: We estimated source-specific air pollution health effects at the city level. Our results show strong variability, emphasising the need for local policies and coordinated actions that consider city-level specificities in source contributions. FUNDING: Spanish Ministry of Science and Innovation, State Research Agency, Generalitat de Catalunya, Centro de Investigación Biomédica en red Epidemiología y Salud Pública, and Urban Burden of Disease Estimation for Policy Making 2023-2026 Horizon Europe project.

Heat-related mortality in Europe during the summer of 2022.

Over 70,000 excess deaths occurred in Europe during the summer of 2003. The resulting societal awareness led to the design and implementation of adaptation strategies to protect at-risk populations. We aimed to quantify heat-related mortality burden during the summer of 2022, the hottest season on record in Europe. We analyzed the Eurostat mortality database, which includes 45,184,044 counts of death from 823 contiguous regions in 35 European countries, representing the whole population of over 543 million people. We estimated 61,672 (95% confidence interval (CI) = 37,643-86,807) heat-related deaths in Europe between 30 May and 4 September 2022. Italy (18,010 deaths; 95% CI = 13,793-22,225), Spain (11,324; 95% CI = 7,908-14,880) and Germany (8,173; 95% CI = 5,374-11,018) had the highest summer heat-related mortality numbers, while Italy (295 deaths per million, 95% CI = 226-364), Greece (280, 95% CI = 201-355), Spain (237, 95% CI = 166-312) and Portugal (211, 95% CI = 162-255) had the highest heat-related mortality rates. Relative to population, we estimated 56% more heat-related deaths in women than men, with higher rates in men aged 0-64 (+41%) and 65-79 (+14%) years, and in women aged 80+ years (+27%). Our results call for a reevaluation and strengthening of existing heat surveillance platforms, prevention plans and long-term adaptation strategies.

Long-term exposure to air pollution and severe COVID-19 in Catalonia: a population-based cohort study.

The association between long-term exposure to ambient air pollutants and severe COVID-19 is uncertain. We followed 4,660,502 adults from the general population in 2020 in Catalonia, Spain. Cox proportional models were fit to evaluate the association between annual averages of PM2.5, NO2, BC, and O3 at each participant's residential address and severe COVID-19. Higher exposure to PM2.5, NO2, and BC was associated with an increased risk of COVID-19 hospitalization, ICU admission, death, and hospital length of stay. An increase of 3.2 µg/m3 of PM2.5 was associated with a 19% (95% CI, 16-21) increase in hospitalizations. An increase of 16.1 µg/m3 of NO2 was associated with a 42% (95% CI, 30-55) increase in ICU admissions. An increase of 0.7 µg/m3 of BC was associated with a 6% (95% CI, 0-13) increase in deaths. O3 was positively associated with severe outcomes when adjusted by NO2. Our study contributes robust evidence that long-term exposure to air pollutants is associated with severe COVID-19.

Long-Term Exposure to Air Pollution and COVID-19 Vaccine Antibody Response in a General Population Cohort (COVICAT Study, Catalonia).

BACKGROUND: Ambient air pollution has been associated with COVID-19 disease severity and antibody response induced by infection. OBJECTIVES: We examined the association between long-term exposure to air pollution and vaccine-induced antibody response. METHODS: This study was nested in an ongoing population-based cohort, COVICAT, the GCAT-Genomes for Life cohort, in Catalonia, Spain, with multiple follow-ups. We drew blood samples in 2021 from 1,090 participants of 2,404 who provided samples in 2020, and we included 927 participants in this analysis. We measured immunoglobulin M (IgM), IgG, and IgA antibodies against five viral-target antigens, including receptor-binding domain (RBD), spike-protein (S), and segment spike-protein (S2) triggered by vaccines available in Spain. We estimated prepandemic (2018-2019) exposure to fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5)], nitrogen dioxide (NO2), black carbon (BC), and ozone (O3) using Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) models. We adjusted estimates for individual- and area-level covariates, time since vaccination, and vaccine doses and type and stratified by infection status. We used generalized additive models to explore the relationship between air pollution and antibodies according to days since vaccination. RESULTS: Among vaccinated persons not infected by SARS-CoV-2 (n=632), higher prepandemic air pollution levels were associated with a lower vaccine antibody response for IgM (1 month post vaccination) and IgG. Percentage change in geometric mean IgG levels per interquartile range of PM2.5 (1.7 µg/m3) were -8.1 (95% CI: -15.9, 0.4) for RBD, -9.9 (-16.2, -3.1) for S, and -8.4 (-13.5, -3.0) for S2. We observed a similar pattern for NO2 and BC and an inverse pattern for O3. Differences in IgG levels by air pollution levels persisted with time since vaccination. We did not observe an association of air pollution with vaccine antibody response among participants with prior infection (n=295). DISCUSSION: Exposure to air pollution was associated with lower COVID-19 vaccine antibody response. The implications of this association on the risk of breakthrough infections require further investigation. https://doi.org/10.1289/EHP11989.