GIS-modeled indicators of traffic-related air pollutants and adverse pulmonary health among children in El Paso, Texas

Wildfire exposure and academic performance in Brazil: A causal inference approach for spatiotemporal data

As the frequency and intensity of wildfires are projected to globally amplify due to climate change, there is a growing need to quantify the impact of exposure to wildfires in vulnerable populations such as adolescents. In our study, we applied rigorous causal inference methods to estimate the effect of wildfire exposure on the academic performance of high school students in Brazil between 2009 and 2015. Using longitudinal data from 8,183 high schools across 1,571 municipalities in Brazil, we estimated that the average performance in most academic subjects decreases under interventions that increase wildfire exposure, e.g., a decrease of 1.8 % (p = 0.01) in the natural sciences when increasing the wildfire density from 0.0035 wildfires/km2 (first quantile in the sample) to 0.0222 wildfires/km2 (third quartile). Furthermore, these effects considerably worsened over time. Our findings highlight the adverse impact of wildfires on educational outcomes.

Long-term exposure to air pollution and lung function among children in China: Association and effect modification

Background

Children are vulnerable to the respiratory effects of air pollution, and their lung function has been associated with long-term exposure to low air pollution level in developed countries. However, the impact of contemporary air pollution level in developing countries as a result of recent efforts to improve air quality on children's lung function is less understood.

Methods

We obtained a cross-sectional sample of 617 schoolchildren living in three differently polluted areas in Anhui province, China. 2-year average concentrations of air pollutants at the year of spirometry and the previous year (2017-2018) obtained from district-level air monitoring stations were used to characterize long-term exposure. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), and forced expiratory flow between 25 and 75% of FVC (FEF_25-75) were determined under strict quality control. Multivariable regression was employed to evaluate the associations between air pollution level and lung function parameters, overall and by demographic characteristics, lifestyle, and vitamin D that was determined by liquid chromatography tandem mass spectrometry.

Results

Mean concentration of fine particulate matter was 44.7 μg/m³, which is slightly above the interim target 1 standard of the World Health Organization. After adjusting for confounders, FVC, FEV₁, and FEF_25-75 showed inverse trends with increasing air pollution levels, with children in high exposure group exhibiting 87.9 [95% confidence interval (CI): 9.5, 166.4] mL decrement in FEV₁ and 195.3 (95% CI: 30.5, 360.1) mL/s decrement in FEF_25-75 compared with those in low exposure group. Additionally, the above negative associations were more pronounced among those who were younger, girls, not exposed to secondhand smoke, non-overweight, physically inactive, or vitamin D deficient.

Conclusions

Our study suggests that long-term exposure to relatively high air pollution was associated with impaired lung function in children. More stringent pollution control measures and intervention strategies accounting for effect modification are needed for vulnerable populations in China and other developing countries.

Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma

Air pollutants are major risk factors for respiratory diseases, particularly asthma, socially and spatially correlated. Many existing environment-asthma-related studies, however, have evaluated the impact of crude trends at the largest district level, which accounts only for temporal effects and may produce biased results with spatial autocorrelation. This study aimed to investigate how the spatial autocorrelation affects the air pollution effect estimations (sulfur dioxide [SO₂], nitrogen dioxide [NO₂], carbon monoxide [CO], and particulate matter [PM₁₀]) on daily asthma emergency department (ED) visits in two metropolitan areas in Korea (Seoul Metropolitan Area [SMA] and Busan Metropolitan City, Ulsan Metropolitan City, Gyeongsangnamdo [BUG]). We applied eigenvector spatial filter (ESF) to the spatio-temporal model to remove spatial autocorrelation and distributed lag nonlinear model (DLNM) to explore nonlinear patterns between air pollutant concentration and lagged days on the three models including aggregated model (a temporal model), spatial model without ESF, and spatial model with ESF (both are spatio-temporal models). The effect of SO₂ was not statistically significant for asthma ED visits in the aggregated model for SMA (cumulative relative risks [CRR] = 0.99, confidence intervals [CI]: 0.93-1.05), while the effect was statistically significant in the spatial model with ESF (CRR = 1.10, CI: 1.08-1.12). NO₂ and CO were positively correlated to asthma ED visits in the spatial model without ESF (CRR = 0.84, CI: 0.81-0.86; 0.91, 0.89-0.94, respectively), but the spatial model with ESF showed significant risks (CRR = 1.21, CI: 1.18-1.24; 1.13, 1.11-1.16). Moreover, the spatial model with ESF successfully removed spatial autocorrelation (P-values for Moran's I 0.83-0.98) and demonstrated the highest model fit (McFadden's pseudo R² 0.42-0.43 for SMA and 0.26-0.27 for BUG) among the three models. Our findings demonstrate how ESF can be introduced into spatial correlation to remove bias and construct more reliable models.

Association between short-term exposure to ambient PM1 and PM2.5 and forced vital capacity in Chinese children and adolescents

This study aims to examine the association between short-term exposure to ambient PM₁, PM_1-2.5, and PM_2.5 and forced vital capacity (FVC). Population data were obtained from a school-based cross-sectional survey in Shandong in 2014. Distributed lag non-linear models were used to examine the association between exposure to PM₁, PM_1-2.5, and PM_2.5 and FVC at the day of FVC measurement and the previous 6 days (lag 0 to 6 days). A total of 35,334 students aged 9 to 18 years were included in the study, and the mean exposure concentrations of ambient PM₁, PM_1-2.5, and PM_2.5 for them were 47.4 (standard deviation [SD] = 21.3) μg/m³, 32.8 (SD = 32.2) μg/m³, and 80.1 (SD = 47.7) μg/m³, respectively. An inter-quartile range (IQR, 24 μg/m³) increment in exposure to PM₁ was significantly associated with a lower FVC at lag 0 and lag 1 day (β =  - 80 mL, 95% CI =  - 119, - 42, and β =  - 37 mL, 95% CI =  - 59, - 16, respectively), and an IQR (54 μg/m³) increment in exposure to PM_2.5 was significantly associated with a lower FVC at lag 0 and lag 1 day (β =  - 57 mL, 95% CI =  - 89, - 18, and β =  - 34 mL, 95% CI =  - 56, - 12, respectively) after adjustment for gender, age, body mass index category, residence, month of the survey, intake of eggs, intake of milk, physical activity, and screen time. No significant associations were observed for PM_1-2.5. The inverse associations of PM₁ and PM_2.5 with FVC were larger in males, younger children, those overweight or obese, and those with insufficient physical activity levels. Short-term exposure to ambient PM₁ and PM_2.5 was associated with decreased FVC, and PM₁ may be the primary fraction of PM_2.5 causing the adverse pulmonary effects. Our findings emphasize the need to address ambient PM, especially PM₁, pollution for affecting pulmonary health in children and adolescents.

Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020

In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.

Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

Schools exposure to air pollution sources in Brazil: A nationwide assessment of more than 180 thousand schools

A growing body of evidence demonstrates that children at schools who are exposed to increased concentrations of air pollutants may have a higher risk for several health problems, including cognitive deficits. In this paper we estimate the exposure to air pollution sources at 186,080 schools in Brazil. Specifically, we accounted for the exposure to three proxies of air pollution source emissions, including distance to roadways, the extent of roadways within a buffer around each school, and the number of wildfire occurrences within a buffer around each school. About 25% of the Brazilian schools evaluated in our study are located within a distance ≤250 m of a major roadway, have ≥2 km of roadway within a buffer of 1 km, and have ≥7 wildfires records within a buffer of 10 km. Our results indicate significant prevalence ratio of these schools exposed to air pollution sources when we stratified the analyses by socioeconomic factors, including geographic (public schools had an increased likelihood of being exposed), economic (low-income areas had an increased likelihood of being exposed), health (overall, areas with low public health status had an increased likelihood of being exposed), and educational conditions (overall, areas with low educational indicator had an increased likelihood of being exposed). For example, we estimated that private schools were 15% (95% CI: 13-17%) less likely to be located within 250 m of a major roadway compared with public schools; schools in areas with low child mortality were 35% (95% CI: 34-37%) less likely to be within 250 m of a major roadway; and schools in regions with low expected years of schooling were 25% (95% CI: 22-28%) more likely to be located within 250 m of a major roadway. The analysis of the spatial patterns shows that a substantial number of schools (36-54%, depending on the air pollution source) has a positive autocorrelation, suggesting that exposure level at these schools are similar to their neighbors. Estimating children's exposure to air pollutants at school is crucial for future public policies to develop effective environmental, transportation, educational, and urban planning interventions that may protect students from exposure to environmental hazards and improve their safety, health, and learning performance.

Heat maps present the spatial distribution of human papillomavirus infection in Zhejiang Province, China

Determining the spatial distribution of human papillomavirus (HPV) and performing accurate public health analyses helps to distinguish areas of healthcare that require further research, and enables therapeutic techniques and approaches in healthcare to be focused more accurately. A total of 4,560 women were enrolled in the present study. Flow-through hybridization and gene chip assays were used to detect the genotypes of HPV infection. Heat maps were then generated to present the spatial distribution of HPV infections in Zhejiang Province according to genotype. Of the exfoliated cervical cell samples from the 4,560 women, HPV was detected in 1,886 samples. HPV-16, -58, -52 and -18 were the most prevalently identified genotypes in the population included in the present study. HPV-16 and -58 infections were mainly distributed in the northern and central regions of Zhejiang Province, such as in Hangzhou and Shaoxing, where the prevalence was higher than that in the southern regions (P<0.05). HPV-18 infection was widespread throughout Zhejiang Province, but had a much lower infection rate in Ningbo and Huzhou (P<0.05). High infection rates of HPV-52 were mainly detected in Hangzhou and the eastern coastal areas of Wenzhou, with a relatively low rate of infection in the center of the province (P<0.05). In conclusion, HPV-16, -58, -52 and -18 were the four most prevalent HPV genotypes observed in Zhejiang Province. Heat maps were created to display the spatial distribution of HPV infection according to genotype, which varied by geographical regions. The results indicate that for individuals in Ningbo or Wenzhou, bivalent or quadrivalent vaccines may be suitable, but for those in Hangzhou and Shaoxing, nonavalent vaccines are strongly recommended.

Asthma-prone areas modeling using a machine learning model

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O₃), sulfur dioxide (SO₂), carbon monoxide (CO), and nitrogen dioxide (NO₂). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

Transforming Our Cities: Best Practices Towards Clean Air and Active Transportation

PURPOSE OF REVIEW

By 2050, 70% of the global population will live in urban areas, exposing a greater number of people to specific city-related health risks that will only be exacerbated by climate change. Two prominent health risks are poor air quality and physical inactivity. We aim to review the literature and state the best practices for clean air and active transportation in urban areas.

RECENT FINDINGS

Cities have been targeting reductions in air pollution and physical inactivity to improve population health. Oslo, Paris, and Madrid plan on banning cars from their city centers to mitigate climate change, reduce vehicle emissions, and increase walking and cycling. Urban streets are being redesigned to accommodate and integrate various modes of transportation to ensure individuals can become actively mobile and healthy. Investments in pedestrian, cycling, and public transport infrastructure and services can both improve air quality and support active transportation. Emerging technologies like electric and autonomous vehicles are being developed and may reduce air pollution but have limited impact on physical activity. Green spaces too can mitigate air pollution and encourage physical activity. Clean air and active transportation overlap considerably as they are both functions of mobility. The best practices of clean air and active transportation have produced impressive results, which are improved when enacted simultaneously in integrated policy packages. Further research is needed in middle- and low-income countries, using measurements from real-world interventions, tracing air pollution back to the sources responsible, and holistically addressing the entire spectrum of exposures and health outcomes related to transportation.

An Exposure Appraisal of Outdoor Air Pollution on the Respiratory Well-being of a Developing City Population

Zaria is the educational hub of northern Nigeria. It is a developing city with a pollution level high enough to be ranked amongst the World Health Organization’s (WHO) most polluted cities. The study appraised the influence of outdoor air pollution on the respiratory well-being of a population in a limited resource environment. With the approved ethics, the techniques utilized were: portable pollutant monitors, respiratory health records, WHO AirQ+ software, and the American Thoracic Society (ATS) questionnaire. They were utilized to acquire day-time weighted outdoor pollution levels, health respiratory cases, assumed baseline incidence (BI), and exposure respiratory symptoms among selected study participants respectively. The study revealed an average respiratory illness incidence rate of 607 per 100,000 cases. Findings showed that an average of 2648 cases could have been avoided if the theoretical WHO threshold limit for the particulate matter with diameter of <2.5/10 micron (PM_2.5/PM₁₀) were adhered to. Using the questionnaire survey, phlegm was identified as the predominant respiratory symptom. A regression analysis showed that the criteria pollutant PM_2.5, was the most predominant cause of respiratory symptoms among interviewed respondents. The study logistics revealed that outdoor pollution is significantly associated with respiratory well-being of the study population in Zaria, Nigeria.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM_2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM_2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.

The role of the indoor environment: Residential determinants of allergy, asthma and pulmonary function in children from a US-Mexico border community

The El Paso Children's Health Study examined environmental risk factors for allergy and asthma among fourth and fifth grade schoolchildren living in a major United States-Mexico border city. Complete questionnaire information was available for 5210 children, while adequate pulmonary function data were available for a subset of 1874. Herein we studied indoor environmental health risk factors for allergy and asthma. Several indoor environmental risk factors were associated with allergy and asthma. In particular, we found that ant and spider pest problems, pet dogs, fireplace heat, central air conditioning, humidifier use, and cooking with gas stoves were positively associated with both allergy and asthma prevalence. With regards to asthma severity, our analysis indicated that exposure to pet dogs increased monotonically with increasing asthma severity while the lack of any heat source and gas stove use for cooking decreased monotonically with increasing asthma severity. Lung function also decreased among children who lived in homes with reported cockroach pest problem in the past year without concurrent use of pesticides. These effects on pulmonary function were present even after excluding children with a current physician's diagnosis of asthma. Clinicians and public health professionals may need to look closely at the contribution of these indoor risk factors on pulmonary health and quality of life among susceptible populations.

Spatio-temporal analysis of particulate matter intake fractions for vehicular emissions: Hourly variation by micro-environments in the Greater Toronto and Hamilton Area, Canada

Previous investigations have reported intake fraction (iF) for different environments, which include ambient concentrations (outdoor exposure) and microenvironments (indoor exposure). However, little is known about iF variations due to space-time factors, especially in microenvironments. In this paper, we performed a spatio-temporal analysis of particulate matter (PM_2.5) intake fractions for vehicular emissions. Specifically, we investigated hourly variation (12:00am-11:00pm) by micro-environments (residences and workplaces) in the Greater Toronto and Hamilton Area (GTHA), Canada. We used GIS modeling to estimate air pollution data (ambient concentration, and traffic emission) and population data in each microenvironment. Our estimates showed that the total iF at residences and workplaces accounts for 85% and 15%, respectively. Workplaces presented the highest 24h average iF (1.06ppm), which accounted for 25% higher than residences. Observing the iF by hour at residences, our estimates showed the highest average iF at 2:00am (iF=3.72ppm). These estimates indicate that approximately 4g of PM_2.5 emitted from motor vehicles are inhaled for every million grams of PM_2.5 emitted. For the workplaces, the highest exposure was observed at 10:00am, with average iF equal to 2.04ppm. The period of the day with the lower average iF for residences was at 8:00am (average iF=0.11ppm), while for the workplaces was at 4:00am (average iF=0.47ppm). Our approach provides a new perspective on human exposure to air pollution. Our results showed significant hourly variation in iF across the GTHA. Our findings can be incorporated in future investigations to advance environmental health effects research and human health risk assessment.

NO2 inhalation enhances asthma susceptibility in a rat model

Nitrogen dioxide (NO₂) is a major air pollutant. Epidemiologic studies have found that NO₂ exposure is associated with an increased risk of asthma. Nevertheless, the potential molecular mechanisms remain unclear. In this study, we investigated the effect of NO₂ inhalation on the occurrence of allergic airway inflammation and its underlying mechanisms. Firstly, male Wistar rats were exposed to 2 and 5 mg/m³ NO₂ (28 days, 5 h/day). The results showed that NO₂ exposure could induce pulmonary inflammatory response, mucus formation, and Th1/Th2 imbalance in the lung of normal rats, resulting in allergic asthma-like features. Secondly, male Wistar rats were exposed to 5 mg/m³ NO₂ (42 days, 5 h/day), sensitized with ovalbumin (OVA), challenged with aerosolized OVA, and characterized in asthma models. Results showed that NO₂ exposure aggravated lung inflammation in the OVA-sensitized rats, accompanied by the increase in inflammatory cell infiltration, mucus hypersecretion, and collagen deposition. Furthermore, NO₂ exposure promoted the increase in the expression of mucin gene (MUC5AC) and pro-inflammatory factors [interleukin (IL)-1β, intercellular adhesion molecule-1 (ICAM-1), and IL-6] as well as serum OVA-specific immunoglobulin E (IgE) production. Taken together, we established that NO₂ exposure promotes allergic airway inflammation and increases the asthma susceptibility. The underlying mechanisms involve the promotion of activation of interleukin-4/signal transducer and activator of transcription-6 (IL-4/STAT6) pathway [IL-4 receptor (IL-4R) α, janus kinase (JAK) 1, JAK 3, and STAT6] and related transcription factor [T cell-specific protein-tyrosine kinase (Lck), extracellular-regulated kinase (ERK)1/2, and nuclear factor-κB (NF-κB)]. In particular, the imbalance of Th1/Th2 cell differentiation [IL-4, interferon (IFN)-γ, GATA-binding protein-3 (GATA-3), and T-box expressed in T cells (T-bet)] plays a pivotal role in NO₂-induced inflammatory responses. These findings may provide a better understanding of mechanism of NO₂-associated respiratory diseases.

Effects of Long-Term Exposure to Traffic-Related Air Pollution on Lung Function in Children

Lung function in early life has been shown to be an important predictor for peak lung function in adults and later decline. Reduced lung function per se is associated with increased morbidity and mortality. With this review, we aim to summarize the current epidemiological evidence on the effect of traffic-related air pollution on lung function in children and adolescents. We focus in particular on time windows of exposure, small airway involvement, and vulnerable sub-groups in the population. Findings from studies published to date support the notion that exposure over the entire childhood age range seems to be of importance for lung function development. We could not find any conclusive data to support evidence of sup-group effects considering gender, sensitization status, and asthma status, although a possibly stronger effect may be present for children with asthma. The long-term effects into adulthood of exposure to air pollution during childhood remains unknown, but current studies suggest that these deficits may be propagated into later life. In addition, further research on the effect of exposure on small airway function is warranted.

Neighborhood-Level Factors Related to Asthma in Children Living in Urban Areas

Asthma disproportionately affects children who are non-White and of low socioeconomic status. One innovative approach to address these health disparities is to investigate the child's neighborhood environment and factors influencing asthma symptoms. The purpose of this integrative review is to critique research investigating the relationships between neighborhood-level factors and asthma morbidity in urban children. Three literature databases were searched using the terms "asthma," "child," "neighborhood," and "urban." The articles included were organized into six themes within the larger domains of prevalence, physical, and social factors. Literature tables provide in-depth analysis of each article and demonstrate a need for strengthening analysis methods. The current research points to the necessity for a multilevel study to analyze neighborhood-level factors that are associated with increased asthma morbidity in urban children. School nurse clinicians, working within children's neighborhoods, are uniquely positioned to assess modifiable neighborhood-level determinants of health in caring for children with asthma.

Spatiotemporal analysis of traffic emissions in over 5000 municipal districts in Brazil

Exposure to traffic emission is harmful to human health. Emission inventories are essential to public health policies aiming at protecting human health, especially in areas with incomplete or nonexistent air pollution monitoring networks. In Brazil, for example, only 1.7% of municipal districts have a monitoring network, and only a few studies have reported data on vehicle emission inventories. No studies have presented emission inventories by municipality. In this study, we predicted vehicular emissions for 5570 municipal districts in Brazil during the period 2001-2012. We used a top-down method to estimate emissions. Carbon dioxide (CO₂) is the pollutant with the highest emissions, with approximately 190 million tons per year during the period 2001-2012). For the other traffic-related pollutants, we predicted annual emissions of 1.5 million tons for carbon monoxide (CO), 1.2 million tons of nitrogen oxides (NO_x), 209,000 tons of nonmethane hydrocarbons (NMHC), 58,000 tons of particulate matter (PM), and 42,000 tons for methane (CH₄). From 2001 to 2012, CO, NMHC, and PM emissions decreased by 41, 33, and 47%, respectively, whereas those CH₄, NO_x, and CO₂ increased by 2, 4, and 84%, respectively. We estimated uncertainties in our study and found that NO_x was the pollutant with the lowest percentage difference, 8%, and NMHC with the highest one, 30%. For CO, CH₄, CO₂, and PM, the values were 22, 14, 21, and 20%, respectively. Finally, we found that during 2001 and 2012 emissions increased in the Northwest and Northeast. In contrast, pollutant emissions, except for CO₂, decreased in the Southeast, South, and part of Midwest. Our predictions can be critical to efforts developing cost-effective public policies tailored to individual municipal districts in Brazil.

IMPLICATIONS

Emission inventories may be an alternative approach to provide data for air quality forecasting in areas where air quality data are not available. This approach can be an effective tool in developing spatially resolved emission inventories.

Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS)

OBJECTIVES

Mississippi (MS) is one of the southern states with high rates of foodborne infections. The objectives of this paper are to determine the extent of Salmonella and Escherichia coli infections in MS, and determine the Salmonella infections correlation with socioeconomic status using geographical information system (GIS) and neural network models.

METHODS

In this study, the relevant updated data of foodborne illness for southern states, from 2002 to 2011, were collected and used in the GIS and neural networks models. Data were collected from the Centers for Disease Control and Prevention (CDC), MS state Department of Health and the other states department of health. The correlation between low socioeconomic status and Salmonella infections were determined using models created by several software packages, including SAS, ArcGIS @RISK and NeuroShell.

RESULTS

Results of this study showed a significant increase in Salmonella outbreaks in MS during the study period, with highest rates in 2011 (47.84 ± 24.41 cases/100,000; p<0.001). MS had the highest rates of Salmonella outbreaks compared with other states (36 ± 6.29 cases/100,000; p<0.001). Regional and district variations in the rates were also observed. GIS maps of Salmonella outbreaks in MS in 2010 and 2011 showed the districts with higher rates of Salmonella. Regression analysis and neural network models showed a moderate correlation between cases of Salmonella infections and low socioeconomic factors. Poverty was shown to have a negative correlation with Salmonella outbreaks (R(2)=0.152, p<0.05).

CONCLUSIONS

Geographic location besides socioeconomic status may contribute to the high rates of Salmonella outbreaks in MS. Understanding the geographical and economic relationship with infectious diseases will help to determine effective methods to reduce outbreaks within low socioeconomic status communities.

REFINING OF ASTHMA PREVALENCE SPATIAL DISTRIBUTION AND VISUALIZATION OF OUTDOOR ENVIRONMENT FACTORS USING GIS AND ITS APPLICATION FOR IDENTIFICATION OF MUTUAL ASSOCIATIONS

AIM

This study presents a procedure of complex assessment of the environment impact on asthma prevalence. This approach is also applicable for any other disease which is supposed to be associated with the quality of the outdoor environment.

METHODS

The input data included asthma prevalence values from the National Institute of Public Health (NIPH) cross-section questionnaire survey (13,456 children) and annual reports on activities of all paediatricians in the Czech Republic (2,072 surgeries); concentrations of PM10, PM2.5, NO2, SO2, O3, benzene, benzo(a)pyrene, As, Cd, Pb and Ni; emissions of total suspended particles, SO2, NOx, CO, VOC, NH3; traffic intensity; land cover (anthropogenic area, urban greenery, arable land, grassland, other agricultural land, forests); proportion of cultivation of individual agricultural crops (17 categories); and proportion of individual woods (15 categories). Using the Geographical Information Systems (GIS) analysis the integration of all source data through one spatial unit was achieved and complete data sets were compiled to be subjected to statistical analysis (combination of factor analysis with logistic/linear regression).

RESULTS

In this study, the approach of combined use of GIS analyses and statistical evaluation of large input data sets was tested. The asthma prevalence demonstrated positive associations with the air pollution (PM10, PM2.5, benzene, benzo(a)pyren, SO2, Pb, Cd) and the rate of agricultural use of land (growing oats, rye, arable fodder crops). Conversely, there was a negative association with the occurrence of natural forests (ash, poplar, fir, beech, spruce, pine). No significant associations were observed with the distance from the road, traffic intensity and NO2 concentration.

CONCLUSIONS

These findings suggest that the outdoor quality may be one of the crucial factors for asthma prevalence.

Long-Term Exposure to Primary Traffic Pollutants and Lung Function in Children: Cross-Sectional Study and Meta-Analysis

Background

There is widespread concern about the possible health effects of traffic-related air pollution. Nitrogen dioxide (NO₂) is a convenient marker of primary pollution. We investigated the associations between lung function and current residential exposure to a range of air pollutants (particularly NO₂, NO, NOx and particulate matter) in London children. Moreover, we placed the results for NO₂ in context with a meta-analysis of published estimates of the association.

Methods and Findings

Associations between primary traffic pollutants and lung function were investigated in 4884 children aged 9–10 years who participated in the Child Heart and Health Study in England (CHASE). A systematic literature search identified 13 studies eligible for inclusion in a meta-analysis. We combined results from the meta-analysis with the distribution of the values of FEV₁ in CHASE to estimate the prevalence of children with abnormal lung function (FEV₁<80% of predicted value) expected under different scenarios of NO₂ exposure. In CHASE, there were non-significant inverse associations between all pollutants except ozone and both FEV1 and FVC. In the meta-analysis, a 10 μg/m³ increase in NO₂ was associated with an 8 ml lower FEV₁ (95% CI: -14 to -1 ml; p: 0.016). The observed effect was not modified by a reported asthma diagnosis. On the basis of these results, a 10 μg/m³ increase in NO₂ level would translate into a 7% (95% CI: 4% to 12%) increase of the prevalence of children with abnormal lung function.

Conclusions

Exposure to traffic pollution may cause a small overall reduction in lung function and increase the prevalence of children with clinically relevant declines in lung function.

Local Variability in the Impacts of Residential Particulate Matter and Pest Exposure on Children's Wheezing Severity: A Geographically Weighted Regression Analysis of Environmental Health Justice

Two assumptions have underpinned environmental justice over the past several decades: 1) uneven environmental exposures yield correspondingly unequal health impacts and 2) these effects are stable across space. To test these assumptions, relationships for residential pest and PM2.5 exposures with children's wheezing severity are examined using global (ordinary least squares) and local (geographically weighted regression [GWR]) models using cross-sectional observational survey data from El Paso (Texas) children. In the global model, having pests and higher levels of PM2.5 were weakly associated with greater wheezing severity. The local model reveals two types of asthmogenic socio-environments where environmental exposures more powerfully predict greater wheezing severity. The first is a lower-income context where children are disproportionately exposed to pests and PM2.5 and the second is a higher-income socio-environment where children are exposed to lower levels of PM2.5, yet PM2.5is counterintuitively associated with more severe wheezing. Findings demonstrate that GWR is a powerful tool for understanding relationships between environmental conditions, social characteristics and health inequalities.

Traffic-related pollution and asthma prevalence in children. Quantification of associations with nitrogen dioxide

Ambient nitrogen dioxide is a widely available measure of traffic-related air pollution and is inconsistently associated with the prevalence of asthma symptoms in children. The use of this relationship to evaluate the health impact of policies affecting traffic management and traffic emissions is limited by the lack of a concentration-response function based on systematic review and meta-analysis of relevant studies. Using systematic methods, we identified papers containing quantitative estimates for nitrogen dioxide and the 12 month period prevalence of asthma symptoms in children in which the exposure contrast was within-community and dominated by traffic pollution. One estimate was selected from each study according to an a priori algorithm. Odds ratios were standardised to 10 μg/m³ and summary estimates were obtained using random- and fixed-effects estimates. Eighteen studies were identified. Concentrations of nitrogen dioxide were estimated for the home address (12) and/or school (8) using a range of methods; land use regression (6), study monitors (6), dispersion modelling (4) and interpolation (2). Fourteen studies showed positive associations but only two associations were statistically significant at the 5 % level. There was moderate heterogeneity (I² = 32.8 %) and the random-effects estimate for the odds ratio was 1.06 (95 % CI 1.00 to 1.11). There was no evidence of small study bias. Individual studies tended to have only weak positive associations between nitrogen dioxide and asthma prevalence but the summary estimate bordered on statistical significance at the 5 % level. Although small, the potential impact on asthma prevalence could be considerable because of the high level of baseline prevalence in many cities. Whether the association is causal or indicates the effects of a correlated pollutant or other confounders, the estimate obtained by the meta-analysis would be appropriate for estimating impacts of traffic pollution on asthma prevalence.

Evaluation of land use regression models for nitrogen dioxide and benzene in Four US cities

Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO₂) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.