Search

Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO 2 ), ozone (O 3 ), and sulphur dioxide (SO 2 ) is related to behavioural and development disorders, anxiety is related to particulate matter (PM 10 ), O 3 and SO 2 , and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used., (© 2024. The Author(s).)

We carried out a search of over 12,000 houses offered on the rental market in Catalonia and assessed the possibility of families below the poverty threshold being able to rent these homes. In this regard, we wanted to evaluate whether the economic situation of families is able to influence their social environment, surroundings, and safety. We observed how their economic situation can allow families the possibility of developing a life without exposure to health risks, and how economic constraints result in disadvantages in several areas of life. The results show how families at risk of poverty live in less favourable conditions and experience a widening of different gaps, with current prices leading to a possible poverty trap for the most disadvantaged groups. The higher the percentage of the population below the threshold, the lower the possibility of not being able to rent a house compared to areas with a lower prevalence of population below the threshold. This association was observed both when considering the risk linearly and non-linearly. Linearly, the probability of not renting a house was reduced by 8.36% for each 1% increase in the prevalence of population at risk of extreme poverty. In the second, third and fourth percentage quartiles, the probability of not being able to rent a house decreased by 21.13%, 48.61%, and 57.79%, respectively. In addition, the effect was different inside and outside of metropolitan areas, with the former showing a decrease of 19.05% in the probability of renting a house, whereas outside metropolitan areas the probability increased by 5.70%.

While most countries have networks of stations for monitoring pollutant concentrations, they do not cover the whole territory continuously. Therefore, to be able to carry out a spatial and temporal study, the predictions for air pollution from unmeasured sites and time periods need to be used. The objective of this study is to predict the air pollutant concentrations of PM 10 , O 3 , NO 2 , SO 2 and CO in sites throughout Catalonia (Spain) and time periods without a monitoring station. Compositional data (CoDa) studies the relative importance of pollutants. A novel feature in this article is combining CoDa with an indicator of total pollution. Predictions are then made using a combination of spatio-temporal models and the Bayesian Laplace Integrated Approach (INLA) inference method. The most relevant results obtained indicate that the log-ratio between NO 2 and O 3 has the highest variance and the best predictive accuracy in time and space. Total pollution levels are second in variance but have low spatial predictive accuracy. Third in variance is the low temporal accuracy found in the log-ratio between SO 2 and the remaining pollutants. Globally, the combination of CoDa and the INLA method is suitable for making effective spatio-temporal predictions of air pollutants., (Copyright © 2021. Published by Elsevier Inc.)

We evaluated the epidemiological evidence on the built environment and its link to childhood obesity, focusing on environmental factors such as traffic noise and air pollution, as well as physical factors potentially driving obesity-related behaviors, such as neighborhood walkability and availability and accessibility of parks and playgrounds. Eligible studies were (i) conducted on human children below the age of 18 years, (ii) focused on body size measurements in childhood, (iii) examined at least one built environment characteristic, (iv) reported effect sizes and associated confidence intervals, and (v) were published in English language. A z test, as alternative to the meta-analysis, was used to quantify associations due to heterogeneity in exposure and outcome definition. We found strong evidence for an association of traffic-related air pollution (nitrogen dioxide and nitrogen oxides exposure, p < 0.001) and built environment characteristics supportive of walking (street intersection density, p < 0.01 and access to parks, p < 0.001) with childhood obesity. We identified a lack of studies that account for interactions between different built environment exposures or verify the role and mechanism of important effect modifiers such as age., (© 2021 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.)

The heterogenous distribution of both COVID-19 incidence and mortality in Catalonia (Spain) during the firsts moths of the pandemic suggests that differences in baseline risk factors across regions might play a relevant role in modulating the outcome of the pandemic. This paper investigates the associations between both COVID-19 incidence and mortality and air pollutant concentration levels, and screens the potential effect of the type of agri-food industry and the overall land use and cover (LULC) at area level. We used a main model with demographic, socioeconomic and comorbidity covariates highlighted in previous research as important predictors. This allowed us to take a glimpse of the independent effect of the explanatory variables when controlled for the main model covariates. Our findings are aligned with previous research showing that the baseline features of the regions in terms of general health status, pollutant concentration levels (here NO 2 and PM 10 ), type of agri-food industry, and type of land use and land cover have modulated the impact of COVID-19 at a regional scale. This study is among the first to explore the associations between COVID-19 and the type of agri-food industry and LULC data using a population-based approach. The results of this paper might serve as the basis to develop new research hypotheses using a more comprehensive approach, highlighting the inequalities of regions in terms of risk factors and their response to COVID-19, as well as fostering public policies towards more resilient and safer environments.

Background: The risk of infection and death by COVID-19 could be associated with a heterogeneous distribution at a small area level of environmental, socioeconomic and demographic factors. Our objective was to investigate, at a small area level, whether long-term exposure to air pollutants increased the risk of COVID-19 incidence and death in Catalonia, Spain, controlling for socioeconomic and demographic factors., Methods: We used a mixed longitudinal ecological design with the study population consisting of small areas in Catalonia for the period February 25 to May 16, 2020. We estimated Generalized Linear Mixed models in which we controlled for a wide range of observed and unobserved confounders as well as spatial and temporal dependence., Results: We have found that long-term exposure to nitrogen dioxide (NO 2 ) and, to a lesser extent, to coarse particles (PM 10 ) have been independent predictors of the spatial spread of COVID-19. For every 1 μm/m 3 above the mean the risk of a positive test case increased by 2.7% (95% credibility interval, ICr: 0.8%, 4.7%) for NO 2 and 3.0% (95% ICr: -1.4%,7.44%) for PM 10 . Regions with levels of NO 2 exposure in the third and fourth quartile had 28.8% and 35.7% greater risk of a death, respectively, than regions located in the first two quartiles., Conclusion: Although it is possible that there are biological mechanisms that explain, at least partially, the association between long-term exposure to air pollutants and COVID-19, we hypothesize that the spatial spread of COVID-19 in Catalonia is attributed to the different ease with which some people, the hosts of the virus, have infected others. That facility depends on the heterogeneous distribution at a small area level of variables such as population density, poor housing and the mobility of its residents, for which exposure to pollutants has been a surrogate., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The hypotheses we intended to contrast were, first, that the most deprived neighborhoods in Barcelona, Spain, present high exposure to environmental hazards (differential exposure) and, secondly, that the health effects of this greater exposure were higher in the most deprived neighborhoods (differential susceptibility). The population studied corresponded to the individuals residing in the neighborhoods of Barcelona in the period 2007-2014. We specified the association between the relative risk of death and environmental hazards and socioeconomic indicators by means of spatio-temporal ecological regressions, formulated as a generalized linear mixed model with Poisson responses. There was a differential exposure (higher in more deprived neighborhoods) in almost all the air pollutants considered, when taken individually. The exposure was higher in the most affluent in the cases of environmental noise. Nevertheless, for both men and women, the risk of dying due to environmental hazards in a very affluent neighborhood is about 30% lower than in a very depressed neighborhood. The effect of environmental hazards was more harmful to the residents of Barcelona's most deprived neighborhoods. This increased susceptibility cannot be attributed to a single problem but rather to a set of environmental hazards that, overall, a neighborhood may present.

Numerous studies showing statistically significant associations between environmental noise and adverse health effects already exist for short-term (over one day at most) and long-term (over a year or more) noise exposure, both for morbidity and (albeit to a lesser extent) mortality. Recently, several studies have shown this association to be independent from confounders, mainly those of air pollutants. However, what has not been addressed is the problem of misalignment (i.e. the exposure data locations and health outcomes have different spatial locations). Without any explicit control of such misalignment inference is seriously compromised. Our objective is to assess the long-term effects of traffic noise on mortality in the city of Barcelona (Spain) during 2004-2007. We take into account the control of confounding, for both air pollution and socioeconomic factors at a contextual level and, in particular, we explicitly address the problem of misalignment. We employed a case-control design with individual data. We used deaths resulting from myocardial infarction, hypertension, or Type II diabetes mellitus in Barcelona between 2004 and 2007 as cases for the study, while for controls we used deaths (likewise in Barcelona and over the same period of time) resulting from AIDS or external causes (e.g. accidental falls, accidental poisoning by psychotropic drugs, drugs of abuse, suicide and self-harm, or injuries resulting from motor vehicle accidents). The controls were matched with the cases by sex and age. We used the annual average equivalent A-weighted sound pressure levels for daytime (7-21h), evening-time (21-23h) and night-time (23-7h), and controlled for the following confounders: i) air pollutants (NO2, PM10 and benzene), ii) material deprivation (at a census tract level) and iii) land use and other spatial variables. We explicitly controlled for heterogeneity (uneven distribution of both response and environmental exposures within an area), spatial dependency (of the observations of the response variables), temporal trends (long-term behaviour of the response variables) and spatial misalignment (between response and environmental exposure locations). We used a fully Bayesian method, through the Integrated Nested Laplace Approximation (INLA). Specifically, we plugged the whole model for the exposure into the health model and obtained a linear predictor defined on the entire spatial domain. Separate analyses were carried out for men and for women. After adjusting for confounders, we found that traffic noise was associated with myocardial infarction mortality along with Type II diabetes mellitus in men (in both cases, odds ratios (OR) were around 1.02) and mortality from hypertension in women (ORs around 1.01). Nevertheless, only in the case of hypertension in women, does the association remain statistically significant for all age groups considered (all ages, ≥65 years and ≥75 years)., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: There is evidence that geographic variability of social health inequalities continues to exist even after individual risk factors have been taken into account. However, relatively few studies have examined the contribution of exposure to air pollutants to those inequalities., Objectives: To study the geographic variability of inequalities in mortality and their associations with socioeconomic and environmental inequalities in small areas of the metropolitan of Barcelona during the period 1994 to 2003., Methods: As in the MEDEA Project, the small area unit was the census tract. Study population consisted of the residents of the metropolitan area of Barcelona. Response variables were all-cause and specific-cause standardized mortality ratio (SMR). Explanatory variables were deprivation index, summarizing socioeconomic variables of the census tracts, and estimates of air pollutant exposures. Bayesian hierarchical models were used in order to reduce the extra variability when using SMR and to assess associations between mortality and deprivation and air pollution., Results: Statistically significant associations with deprivation were found for the causes of death related to consumption of tobacco and alcohol for men and, besides lung cancer, diet-related causes for women. Statistically significant pollution coefficients were only found in the metropolitan area of Barcelona and in men. A positive interaction between pollutants and the deprivation index was statistically significant for respiratory mortality and PM(10), and ischemic disease mortality and NO(2), both for men., Conclusions: We found deprivation to be associated in a statistically significant way with the geographical variation in mortality in the census tracts of the metropolitan area of Barcelona, in the period 1994 to 2003. Those air pollutants more directly related with traffic modify some of these associations.

Background, Aim, and Scope: Ten years of public health interventions on industrial emissions to clean air were monitored for the Mediterranean city of Cartagena. During the 1960s, a number of large chemical and non-ferrous metallurgical factories were established that significantly deteriorated the city's air quality. By the 1970s, the average annual air concentration of sulfur dioxide (SO2) ranged from 200 to 300 microg/m3 (standard conditions units). In 1979, the Spanish government implemented an industrial intervention plan to improve the performance of factories and industrial air pollution surveillance. Unplanned urban development led to residential housing being located adjacent to three major factories. Factory A produced lead, factory B processed zinc from ore concentrates, and factory C produced sulfuric acid and phosphates. This, in combination with the particular abrupt topography and frequent atmospheric thermal inversions, resulted in the worsening of air quality and heightening concern for public health. In 1990, the City Council authorized the immediate intervention at these factories to reduce or shut down production if ambient levels of SO2 or total suspended particles (TSP) exceeded a time-emission threshold in pre-established meteorological contexts. The aim of this research was to assess the appropriateness and effectiveness of the intervention plan implemented from 1992 to 2001 to abate industrial air pollution., Materials and Methods: The maximum daily 1-h ambient air level of SO2, NO2, and TSP pollutants was selected from one of the three urban automatic stations, designed to monitor ambient air quality around industrial emissions sources. The day on which an intervention took place to reduce and/or interrupt industrial production by factory and pollutant was defined as a control day, and the day after an intervention as a post-control day. To assess the short-term intervention effect on air quality, an ecological time series design was applied, using regression analysis in generalized additive models, focusing on day-to-day variations of ambient air pollutants levels. Two indicators were estimated: (a) appropriateness, the ratio between mean levels of the pollutant for control days versus the other days, and (b) effectiveness, the ratio between mean levels of the pollutant for post-control days versus the other days. Ratios in regression analyses were adjusted for trend, seasonality, temperature, humidity and atmospheric pressure, calendar day, and special events as well as the other pollutants., Results: A total of 702 control days were made on the factories' industrial production during the 10-year period. Fifteen reductions and five shutdown control days took place at factory A for ambient air SO2. At factory B, more controls were carried out for the SO2 pollutant in the years 1992-1993 and 1997. At factory C, the control days for SO2 decreased from 59 reductions and 14 shutdowns to a minimum from 1995 onwards, whereas the controls on TSP were more frequent, reaching a maximum of 99 reductions and 47 shutdowns in the last year. SO2 ambient air mean levels ranged from 456 to 699 microg/m(3) among factories on reduction control days and between 624 and 1,010 microg/m(3) on shutdown days. The TSP ambient air mean levels were 428 and 506 microg/m(3) on reduction and shutdown days, respectively. For all types of control days and factories, a mean ratio of 104% (95% confidence interval [CI] 88 to 121) in SO(2) levels was obtained and a mean ratio of 67% (95% CI 59 to 75) in TSP levels. Post-control days at all factories showed a mean ratio of -16% (95% CI -7 to -24) in SO(2) levels and a mean ratio of -13% (95% CI -7 to -19) in TSP levels., Discussion: Interventions on industrial production based on the urban SO(2) and TSP ambient air levels were justified by the high concentrations detected. The best assessment of the interventions' effectiveness would have been to utilize the ambient air pollutant concentration readings from the entire time of the production shutdowns or reductions; however, the daily hourly maximum turned out to be a useful indicator because of meteorological factors influencing the diurnal concentration profile. A substantial number of interventions were carried out from 1 to 3 AM: , when vehicular traffic was minimum. On the other hand, atmospheric stability undergoes diurnal cycling in the autumn-winter period due to thermal inversion, which reaches maximum levels around daybreak. Therefore, this increases the ambient air levels and justified the interventions carried out at daybreak in spite of the traffic influence., Conclusions: All the interventions for SO(2) and TSP were carried out when the measured ambient air levels of pollutants were exceeded, which shows the appropriateness of the intervention program. This excess was greater when intervening on SO(2) than on the TSP levels. For both ambient air levels of SO(2) and TSP, significant drops in air pollution were achieved from all three factories following activity reductions. The production shutdown controls were very effective, because they returned excess levels, higher than in the reduction controls, to everyday mean values., Recommendations and Perspectives: The Cartagena City observational system of intermittent control has proven to effectively reduce industrial emissions' impact on ambient air quality. This experienced model approach could serve well in highly polluted industrial settings. From a public health perspective, studies are needed to assess that the industrial interventions to control air pollution were related to healthier human populations. Legislation was needed to allow the public administration to take direct actions upon the polluting industries.

Unlabelled: The EMECAM Project demonstrated the short-term effect of air pollution on the death rate in 14 cities in Spain throughout the 1990-1995 period. The Spanish Multicentre Study on Health Effects of Air Pollution (EMECAS) is broadening these objectives by incorporating more recent data, information on hospital disease admissions and totaling 16 Spanish cities. This is an ecological time series study in which the response variables are the daily deaths and the emergency hospitalizations due to circulatory system diseases and respiratory diseases among the residents in each city. Pollutants analyses: suspended particles, SO2, NO2, CO and O3. Control variables: meteorological, calendar, seasonality and influenza trend and incidence., Statistical Analysis: estimate of the association in each city by means of the construction of generalized additive Poisson regression models and metanalysis for obtaining combined estimators. The EMECAS Project began with the creation of three working groups (Exposure, Epidemiology and Analysis Methodology) which defined the protocol. The average levels of pollutants were below those established under the current regulations for sulfur dioxide, carbon monoxide and ozone. The NO2 and PM10 values were around those established under the regulations (40 mg/m3). This is the first study of the relationship between air pollution and disease rate among one group of Spanish cities. The pollution levels studied are moderate for some pollutants, although for others, especially NO2 and particles, these levels could entail a problem with regard to complying with the regulations in force.

Background A number of factors contribute to attention deficit hyperactivity disorder (ADHD) and although they are not fully known, the occurrence of ADHD seems to be a consequence of an interaction between multiple genetic and environmental factors. However, apart from pesticides, the evidence is inadequate and inconsistent as it differs not only in the population and time period analysed, but also in the type of study, the control of the confounding variables and the statistical methods used. In the latter case, the studies also differ in the adjustment of spatial and temporal variability. Our objective here, is to provide evidence on an association between environmental factors and ADHD. Methods In our study, we used a population-based retrospective cohort in which we matched cases and controls (children free of the disease) by sex and year of birth (n = 5193, 78.9% boys). The cases were children born between 1998 and 2012 and diagnosed with ADHD (n = 116). To evaluate whether there was a geographical pattern in the incidence of ADHD, we first represented the smoothed standardized incidence rates on a map of the region being studied. We then estimated the probability of being a case by using a generalized liner mixed model with a binomial link. As explanatory variables of interest, we included the following environmental variables: distance to agricultural areas, distance to roads (stratified into three categories according to traffic density and intensity), distance to petrol stations, distance to industrial estates, and land use. We control for both observed (individual and family specific variables and deprivation index) and unobserved confounders (in particular, individual and familial heterogeneity). In addition, we adjusted for spatial extra variability. Results We found a north-south pattern containing two clusters (one in the centre of the study region and another in the south) in relation to the risk of developing ADHD. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables. Specifically, living within 100 m from an agricultural area or a residential street and/or living fewer than 300 m from a motorway, dual carriageway or one of the industrial estates analysed was associated (statistically significant) with an increased risk of ADHD. Conclusion Our results indicate that some environmental factors could be associated with ADHD occurring, particularly those associated with exposure to pesticides, organochlorine compounds and air pollutants because of traffic. [ABSTRACT FROM AUTHOR]

Our objective in this work was to present a hierarchical Bayesian spatiotemporal model that allowed us to make spatial predictions of air pollution levels effectively and with very few computational costs. We specified a hierarchical spatiotemporal model using the Stochastic Partial Differential Equations of the integrated nested Laplace approximations approximation. This approach allowed us to spatially predict in the territory of Catalonia (Spain) the levels of the four pollutants for which there is the most evidence of an adverse health effect. Our model allowed us to make fairly accurate spatial predictions of both long-term and short-term exposure to air pollutants with a relatively low density of monitoring stations and at a much lower computation time. The only requirements of our method are the minimum number of stations distributed throughout the territory where the predictions are to be made, and that the spatial and temporal dimensions are either independent or separable. [Display omitted] • We show a hierarchical Bayesian spatiotemporal model. • Our model provides predictions of both long-term and short-term exposure. • The computational cost is low. • The model only needs a minimum number of stations being distributed throughout the territory. • The other requirement of our model is that the spatial and temporal dimensions are either independent or separable. [ABSTRACT FROM AUTHOR]

The article focuses on a study which modeled the spatial distribution of nitrogen dioxide and of particles with diameter of less than 10µm in two areas, the metropolitan areas of Barcelona (MABa) and Bilbao (MABi). It was observed that the census tracks with greater concentration of pollutants are those that were estimated more accurately. Results suggested the existence of a persistent emission source in the tracks.