Effects of Particulate Matter and Its Chemical Constituents on Elderly Hospital Admissions Due to Circulatory and Respiratory Diseases

Benefits of Short-term Premature Mortality Reduction Attributed to PM2.5 Pollution: A Case Study in Long an Province, Vietnam

PM2.5 pollution exposure is the leading cause of disease burden globally, especially in low- and middle-income countries, including Vietnam. Therefore, economic damage in this context must be quantified. Long An province in the Southern Key Economic (SKE) region was selected as a research area. This study aimed to evaluate PM2.5-related human health effects causing early deaths attributable to respiratory, cardiovascular, and circulatory diseases in all ages and genders. Health end-points and health impact estimation, economic loss model, groups of PM2.5 concentration data, data of exposed population, data of baseline premature mortality rate, and data of health impact functions were used. Hourly PM2.5 concentration data sets were generated specifically using the coupled Weather Research and Forecasting Model (WRF)/Community Multiscale Air Quality Modelling System (CMAQ) models. Daily PM2.5 pollution levels considered mainly in the dry season (from January to April 2018) resulted in 12.9 (95% CI - 0.6; 18.7) all-cause premature deaths per 100,000 population, of which 7.8 (95% CI 1.1; 7.1), 1.5 (95% CI - 0.2; 3.1), and 3.6 (95% CI - 1.5; 8.5) were due to respiratory diseases (RDs; 60.54%), cardiovascular diseases (CVDs; 11.81%), and circulatory system diseases (CSDs; 27.65%) per 100,000 population, respectively. The total economic losses due to acute PM2.5 exposure-related premature mortality cases reached 62.0 (95% CI - 2.7; 89.6) billion VND, equivalent to 8.3 (95% CI - 0.4; 12.0) million USD. The study outcomes contributed remarkably to the generation and development of data sources for effectively managing ambient air quality in Long An.

New concentration-response functions for seven morbidity endpoints associated with short-term PM2.5 exposure and their implications for health impact assessment

BACKGROUND

Morbidity burdens from ambient air pollution are associated with market and non-market costs and are therefore important for policymaking. The estimation of morbidity burdens is based on concentration-response functions (CRFs). Most existing CRFs for short-term exposures to PM2.5 assume a fixed risk estimate as a log-linear function over an extrapolated exposure range, based on evidence primarily from Europe and North America.

OBJECTIVES

We revisit these CRFs by performing a systematic review for seven morbidity endpoints previously assessed by the World Health Organization, including data from all available regions. These endpoints include all cardiovascular hospital admission, all respiratory hospital admission, asthma hospital admission and emergency room visit, along with the outcomes that stem from morbidity, such as lost work days, respiratory restricted activity days, and child bronchitis symptom days.

METHODS

We estimate CRFs for each endpoint, using both a log-linear model and a nonlinear model that includes additional parameters to better fit evidence from high-exposure regions. We quantify uncertainties associated with these CRFs through randomization and Monte Carlo simulations.

RESULTS

The CRFs in this study show reduced model uncertainty compared with previous CRFs in all endpoints. The nonlinear CRFs produce more than doubled global estimates on average, depending on the endpoint. Overall, we assess that our CRFs can be used to provide policy analysis of air pollution impacts at the global scale. It is however important to note that improvement of CRFs requires observations over a wide range of conditions, and current available literature is still limited.

DISCUSSION

The higher estimates produced by the nonlinear CRFs indicates the possibility of a large underestimation in current assessments of the morbidity impacts attributable to air pollution. Further studies should be pursued to better constrain the CRFs studied here, and to better characterize the causal relationship between exposures to PM2.5 and morbidity outcomes.

A Multi-Pollutant and Meteorological Analysis of Cardiorespiratory Mortality among the Elderly in São Paulo, Brazil-An Artificial Neural Networks Approach

Traditionally, studies that associate air pollution with health effects relate individual pollutants to outcomes such as mortality or hospital admissions. However, models capable of analyzing the effects resulting from the atmosphere mixture are demanded. In this study, multilayer perceptron neural networks were evaluated to associate PM₁₀, NO₂, and SO₂ concentrations, temperature, wind speed, and relative air humidity with cardiorespiratory mortality among the elderly in São Paulo, Brazil. Daily data from 2007 to 2019 were considered and different numbers of neurons on the hidden layer, algorithms, and a combination of activation functions were tested. The best-fitted artificial neural network (ANN) resulted in a MAPE equal to 13.46%. When individual season data were analyzed, the MAPE decreased to 11%. The most influential variables in cardiorespiratory mortality among the elderly were PM₁₀ and NO₂ concentrations. The relative humidity variable is more important during the dry season, and temperature is more important during the rainy season. The models were not subjected to the multicollinearity issue as with classical regression models. The use of ANNs to relate air quality to health outcomes is still very incipient, and this work highlights that it is a powerful tool that should be further explored.

A causal modelling framework for short-term effects of PM2.5 on hospitalisations: A nationwide time series study in Brazil

Accurate estimates of the causal effect of air pollution on health outcomes, are critical when calculating attributable disease burdens. Brazil has a large population exposed to fast-growing emissions of air pollutants, however no national level studies have been conducted to examine the causal effect of PM_2.5 exposure on health outcomes. This study proposes a novel approach, to accurately estimate the causal relationship between daily PM_2.5 exposure and hospitalisations, across 1,814 Brazilian cities during 2000-2015. A variant of the difference-in-differences (DID) approach was applied under a counterfactual framework. Daily time series data were divided into panels. Seasonality and long-term trend were controlled using indicators for the panel. Variables which do not change within a short-period were controlled using a dummy variable for the day. Controls for variables which vary day by day, were included in the model. We found the proposed model exhibited competitive power performance in detecting causal associations between short-term PM_2.5 exposure and hospitalisations in Brazil. A 10 μg/m³ increase in PM_2.5 concentrations over four days (lag 0-3) was associated with a 1.06 % (95 % CI: 0.94 to 1.17) increase in all-cause hospitalisations and accounted for 1.26 % (95 % CI: 1.12-1.39) of total hospitalisations. Larger effects were found for children aged 0-4 years and the elderly aged 80+ years, suggesting policies should be developed to minimise the exposure of these age groups.

Evaluation of the efficiency of a Venturi scrubber in particulate matter collection smaller than 2.5 µm emitted by biomass burning

Energy demand has increased worldwide, and biomass burning is one of the solutions most used by industries, especially in countries that have a great potential in agriculture, such as Brazil. However, these energy sources generate pollutants, consisting of particulate matter (PM) with a complex chemical composition, such as sugarcane bagasse (SB) burning. Controlling these emissions is necessary; therefore, the aim was to evaluate PM collection using a rectangular Venturi scrubber (RVS), and its effects on the composition of the PM emitted. Considering the appropriate use of biomass as an industrial fuel and the emerging need for a technique capable of efficiently removing pollutants from biomass burning, this study shows the control of emissions as an innovation in a situation such as the industrial one with the use of a Venturi scrubber in fine particle collection, in addition to using portable and representative isokinetic sampling equipment of these particles. The pilot-scale simulation of the biomass burning process, the representative sampling of fine particles and obtaining parameters to control pollutant emissions for a Venturi scrubber, meets the current situation of concern about air quality. The average collection efficiency values were 96.6% for PM_{> 2.5}, 85.5% for PM_1.0-2.5, and 66.9% for PM_{< 1.0}. The ionic analysis for PM_{< 1.0} filters showed potassium, chloride, nitrate, and nitrite at concentrations ranging from 20.12 to 36.5 μg/m³. As the ethanol and sugar plants will continue to generate electricity with sugarcane bagasse burning, emission control technologies and cost-effective and efficient portable samplers are needed to monitor particulate materials and improve current gas cleaning equipment projects.

Long-term PM2.5 exposure in association with chronic respiratory diseases morbidity: A cohort study in Northern China

Several literatures have examined the risk of chronic respiratory diseases in association with short-term ambient PM2.5 exposure in China. However, little evidence has examined the chronic impacts of PM2.5 exposure on morbidity of chronic respiratory diseases in cohorts from high pollution countries. Our study aims to investigate the associations. Based on a retrospective cohort among adults in northern China, a Cox regression model with time-varying PM2.5 exposure and a concentration-response (C-R) curve model were performed to access the relationships between incidence of chronic respiratory diseases and long-term PM2.5 exposure during a mean follow-up time of 9.8 years. Individual annual average PM2.5 estimates were obtained from a satellite-based model with high resolution. The incident date of a chronic respiratory disease was identified according to self-reported physician diagnosis time and/or intake of medication for treatment. Among 38,047 urban subjects analyzed in all-cause chronic respiratory disease cohort, 482 developed new cases. In CB (38,369), asthma (38,783), and COPD (38,921) cohorts, the onsets were 276, 89, and 14, respectively. After multivariable adjustment, hazard ratio and 95% confidence interval for morbidity of all-cause chronic respiratory disease, CB, asthma, and COPD were 1.15 (1.01, 1.31), 1.20 (1.00, 1.42), 0.76 (0.55, 1.04), and 0.66 (0.29, 1.47) with each 10 μg/m3 increment in PM2.5, respectively. Stronger effect estimates were suggested in alcohol drinkers across stratified analyses. Additionally, the shape of C-R curve showed an increasing linear relationship before 75.00 μg/m3 concentrations of PM2.5 for new-onset all-cause chronic respiratory disease, and leveled off at higher levels. These findings indicated that long-term exposure to high-level PM2.5 increased the risks of incident chronic respiratory diseases in China. Further evidence of C-R curves is warranted to clarify the associations of adverse chronic respiratory outcomes involving air pollution.

Influence of sex, season and environmental air quality on experimental human pneumococcal carriage acquisition: a retrospective cohort analysis

Streptococcus pneumoniae (pneumococcus) is the most commonly identified bacterial cause of pneumonia and the leading infectious cause of death in children under 5 years of age worldwide. Pneumococcal disease follows a seasonal pattern with increased incidence during winter. Pneumonia burden is also associated with poor air quality. Nasopharyngeal carriage of the bacterium is a pre-requisite of invasive disease. We aimed to determine if susceptibility to nasopharyngeal pneumococcal carriage varied by season and which environmental factors might explain such variation. We also evaluated the influence of sex on susceptibility of carriage. We collated data from five studies in which human volunteers underwent intranasal pneumococcal challenge. Generalised linear mixed-effects models were used to identify factors associated with altered risk of carriage acquisition, specifically climate and air-quality data. During 2011-2017, 374 healthy adults were challenged with type 6B pneumococcus. Odds of carriage were significantly lower in males (OR, 0.61; 95% CI, 0.40-0.92; p=0.02), and higher with cooler temperatures (OR, 0.79; 95% CI, 0.63-0.99; p=0.04). Likelihood of carriage was also associated with lower concentrations of local fine particulate matter concentrations (PM2.5) and increased local rainfall. In contrast to epidemiological series, experimental challenge allowed us to test propensity to acquisition during controlled exposures; immunological explanations for sex and climatic differences should be sought.

Oxidative stress-mediated particulate matter affects the risk of relapse in schizophrenia patients: Air purification intervention-based panel study

Particulate matter (PM) exposure increased the risk of hospital admission and was related to symptoms of schizophrenia (SCZ). However, there are limited studies on the relationship between PM exposure and SCZ relapse risk, and the underlying biological mechanisms remain unclear. We designed an air purification intervention study under a 16-day real air purifier scenario and another 16-day sham air purifier scenario, with a 2-day washout period. Twenty-four chronic stable male patients were recruited. The oxidative stress biomarkers were measured including serum catalase (CAT), superoxide dismutase (SOD), total antioxidant capacity (T-AOC), malondialdehyde (MDA), and nitric oxide (NO). The relapse risk was evaluated by the early signs scale (ESS). Linear mixed effect models were fitted to establish the associations between PM exposure and ESS and oxidative stress. Mediation model was performed to explore the mediation effect of oxidative stress on the PM-ESS association. Higher concentrations of PM_2.5/PM₁₀ exposure were associated with an elevated risk of relapse of SCZ. For each 10 μg/m³ in PM_2.5 concentration, the scores of ESS and subscales of incipient psychosis (ESS-IP), depression/withdrawal (ESS-N), anxiety/agitation (ESS-A), and excitability/disinhibition (ESS-D) were increased by 4.112 (95% CI: 3.174, 5.050), 1.516 (95%CI: 1.178, 1.853), 1.143 (95%CI: 0.598, 1.689), 1.176 (95%CI: 0.727, 1.625) and 0.238 (95%CI: 0.013, 0.464), while logCAT, SOD and T-AOC were reduced by 0.039 U/ml (95% CI: 0.017, 0.060), 1.258 U/ml (95% CI: 0.541, 1.975), and 0.076 mmol/l (95% CI: 0.026, 0.126). In addition, pathways of "PM_2.5→T-AOC→ESS-A″ and "PM_2.5→T-AOC→ESS-D″ were found, with significant T-AOC mediated effects 15.70% (P = 0.02) and 52.99% (P = 0.04). Our findings suggest that PM may increase the risk of anxiety, depression, excitability, and incipient psychosis behaviors in SCZ patients, while reducing the function of the antioxidant system. The decrease of T-AOC may medicate the PM-ESS association in SCZ.

Low-cost Gent type sampler constructed for urban atmospheric aerosol sampling

The importance of studying the atmospheric pollution due to its effects on human health and other ecosystems, the inexistence of national production of equipment for air sample collection, and the high cost of the imported equipment (especially in developing countries) led the authors of the present work to construct a low-cost Gent type sampler. The construction of the sampler was carried out by combining low-cost materials with good mechanical strength (such as nylon 6.0), hydraulic piping PVC, and the use of a 3D printer. The innovation of the present work is the employment of a 3D printer using ABS polymer to create the grids that cannot be machined. In addition to the sampler, the system is composed of a vacuum pump, a gas meter, and a rotameter. The total cost of the sampling system amounted at about 1200 USD, and the cost of the manufactured Gent type sampler did not reach 100 USD. The results obtained while using this set for sampling atmospheric aerosol for a period of 11 months were compared with the mass concentration of PM₁₀ obtained from the official environmental company, CETESB of São Paulo State, Brazil, showing good correlation with those from CETESB - which confirmed its effectiveness and suitability for use. The low cost, easy operation, and versatility of the built Gent type sampler enable its use for scientific and academic purposes. The equipment can be useful in environmental monitoring networks, in low-income regions, and as an instrument for environmental education used in universities.

Does exposure to air pollution increase the risk of acute care in young children with asthma? An Ontario, Canada study

Owing to their greater outdoor activity and ongoing lung development, children are particularly vulnerable to the harmful effects of exposure to fine particulate matter (PM_2.5)_. However, the effects of PM_2.5 components are poorly understood. This study aimed to use a longitudinal birth cohort of children with physician-diagnosed incident asthma to investigate the effect of PM_2.5 components at birth on morbidity measured by health services utilization. Of 1277 Toronto Child Health Evaluation Questionnaire (T-CHEQ) participants, the study population included 362 children diagnosed with asthma who were followed for a mean of 13 years from birth until March 31, 2016, or loss-to-follow-up. Concentrations of PM_2.5 and its components were assigned based on participants' postal codes at birth. Study outcomes included counts of asthma, asthma-related, and all-cause health services use. Poisson regression in single-, two-, and multi-pollutant models was used to estimate rate ratios (RR) per interquartile range (IQR) increase of exposures. Covariates were included in all models to further adjust for potential confounding. The adjusted RR for sulfate (SO₄) and all-cause hospitalizations was statistically significant with RR = 2.23 (95% confidence interval [CI]: 1.25-3.96) in a multi-pollutant model with nitrogen dioxide (NO₂) and ozone (O₃). In multi-pollutant models with oxidants, the adjusted RRs for SO₄ of all-cause hospitalizations and emergency department (ED) visits were also statistically significant with RR = 2.31 (95% CI: 1.32-4.03) and RR = 1.39 (95% CI: 1.02-1.90), respectively. While unadjusted single-pollutant RRs for asthma-specific and asthma-related health services use with the SO₄ component of PM_2.5 were above one, none were statistically significant. This study found significant associations with exposure to SO₄ in PM_2.5 and all-cause acute care, chiefly for hospitalizations, in children with asthma.

Associations of fine particulate matter and constituents with pediatric emergency room visits for respiratory diseases in Shanghai, China

BACKGROUND

Although ambient fine particulate matter (PM_2.5) has been associated with adverse respiratory outcomes in children, few studies have examined PM_2.5 constituents with respiratory diseases in children in China.

OBJECTIVES

To investigate the associations of short-term exposure to PM_2.5 and its constituents with pediatric emergency room visits (ERVs) for respiratory diseases in Shanghai, China.

METHODS

We collected daily concentrations of PM_2.5 and its constituents in urban Shanghai from January 1, 2016, to December 31, 2018. Daily pediatric ERVs for four major respiratory diseases, including upper respiratory tract infection, bronchitis, pneumonia, and asthma, were obtained from 66 hospitals in Shanghai during the same period. Associations of exposure to daily PM_2.5 and constituents with respiratory ERVs were estimated using the over-dispersed generalized additive models.

RESULT

Short-term exposure to PM_2.5 and its constituents were associated with increased pediatric ERVs for respiratory diseases. Specifically, an interquartile range increase in the 3-day average PM_2.5 level (31 μg/m³) was associated with 1.86% (95%CI: 0.52, 3.22), 1.53% (95%CI: 0.01, 3.08), 1.90% (95%CI: 0.30, 3.52), and 2.67% (95%CI: 0.70, 4.68) increase of upper respiratory tract infection, bronchitis, pneumonia, and asthma ERVs, respectively. As for PM_2.5 constituents, we found organic carbon, ammonium, nitrate, selenium, and zinc were associated with higher risk of respiratory ERVs in the single constituent and the constituent-PM_2.5 models.

CONCLUSION

Short-term exposure to PM_2.5 was associated with increased pediatric ERVs for respiratory diseases. Constituents related to anthropogenic combustion and traffic might be the dominant contributors of the observed associations.

Association between air quality, meteorological factors and COVID-19 infection case numbers

The coronavirus disease (COVID-19) has become a global pandemic affecting many countries, including Singapore. Previous studies have investigated the relationship of air pollutant levels and meteorological factors with respiratory disease risk and hospital admission rates. However, associations between air pollutant concentrations and meteorological factors with COVID-19 infection have been equivocal. This study aimed to assess the association between core air pollutant concentrations, meteorological variables and daily confirmed COVID-19 case numbers in Singapore. Data on air pollutant levels (particulate matter [PM_2.5, PM₁₀], ozone [O₃], carbon monoxide [CO], nitrogen dioxide [NO₂], sulphur dioxide [SO₂], pollutant standards index [PSI]) and meteorological factors (rainfall, humidity, temperature) was obtained from the Singapore National Environment Agency (NEA) from January 23, 2020 to April 6, 2020. The daily reported COVID-19 case numbers were retrieved from the Singapore Ministry of Health (MOH). Generalized linear models with Poisson family distribution and log-link were used to estimate the model coefficients and 95% confidence intervals (CIs) for the association between air pollutant concentrations and meteorological factors (8-day and 15-day moving averages (MA)) with COVID-19 case numbers, adjusting for humidity, rainfall and day of week. We observed significantly positive associations between NO₂, PSI, PM_2.5 and temperature with COVID-19 case numbers. Every 1-unit increase (15-day MA) in PSI, 1 μg/m³ increase (15-day MA) in PM_2.5, NO₂ and 0.1 °C increase in temperature were significantly associated with a 35.0% (95% CI: 29.7%-40.5%), 22.6% (95% CI: 12.0%-34.3%), 34.8% (95% CI: 29.3%-40.4%) and 28.6% (95% CI: 25.0%-32.4%) increase in the average daily number of COVID-19 cases respectively. On the contrary, PM₁₀, O₃, SO₂, CO, rainfall and humidity were significantly associated with lower average daily numbers of confirmed COVID-19 cases. Similar associations were observed for the 8-day MAs. Future studies could explore the long-term consequences of the air pollutants on COVID-19 infection and recovery.

Short-Term Effects of Air Pollution on Cardiovascular Hospitalizations in the Pisan Longitudinal Study

Air pollution effects on cardiovascular hospitalizations in small urban/suburban areas have been scantly investigated. Such effects were assessed among the participants in the analytical epidemiological survey carried out in Pisa and Cascina, Tuscany, Italy (2009–2011). Cardiovascular hospitalizations from 1585 subjects were followed up (2011–2015). Daily mean pollutant concentrations were estimated through random forests at 1 km (particulate matter: PM₁₀, 2011–2015; PM_2.5, 2013–2015) and 200 m (PM₁₀, PM_2.5, NO₂, O₃, 2013–2015) resolutions. Exposure effects were estimated using the case-crossover design and conditional logistic regression (odds ratio—OR—and 95% confidence interval—CI—for 10 μg/m³ increase; lag 0–6). During the period 2011–2015 (137 hospitalizations), a significant effect at lag 0 was observed for PM₁₀ (OR = 1.137, CI: 1.023–1.264) at 1 km resolution. During the period 2013–2015 (69 hospitalizations), significant effects at lag 0 were observed for PM₁₀ (OR = 1.268, CI: 1.085–1.483) and PM_2.5 (OR = 1.273, CI: 1.053–1.540) at 1 km resolution, as well as for PM₁₀ (OR = 1.365, CI: 1.103–1.690), PM_2.5 (OR = 1.264, CI: 1.006–1.589) and NO₂ (OR = 1.477, CI: 1.058–2.061) at 200 m resolution; significant effects were observed up to lag 2. Larger ORs were observed in males and in subjects reporting pre-existent cardiovascular/respiratory diseases. Combining analytical and routine epidemiological data with high-resolution pollutant estimates provides new insights on acute cardiovascular effects in the general population and in potentially susceptible subgroups living in small urban/suburban areas.

Avoiding hospital admissions for respiratory system diseases by complying to the final Brazilian air quality standard: an estimate for Brazilian southeast capitals

According to the World Health Organization (WHO), in 2016, 91% of the global population was living in places where guidelines on air quality were not met, which results in an estimated figure of seven million deaths annually. The new Brazilian air quality standards, CONAMA 491/2018, was the first revision in over two decades and has as final target the WHO guidelines for air quality, although no deadline has been established for implementation. The goal of this work was to quantify public health gains of this new policy based on hospitalizations due to respiratory diseases, the most studied outcome in Brazilian time series studies, in four Brazilian Southeast capitals: São Paulo (SP), Rio de Janeiro (RJ), Belo Horizonte (MG), and Vitória (ES) for PM₁₀, PM_2,5, SO₂, CO, and O₃. Population and hospitalizations data for all respiratory diseases for people under 5 years old, over 64 years old, most vulnerable populations, and all ages were analyzed. The air quality monitoring data was analyzed in two different periods: 2016 to 2018 for São Paulo and Vitória; and between 2015 and 2017 for Belo Horizonte and Rio de Janeiro, according to available monitoring data. A literature review was carried out to determine the appropriate relative risk to be used in the estimations, and the public health gains were calculated based on the selected relative risks for each city. The highest estimate was for São Paulo, with 3454 avoidable respiratory hospital admissions (all ages). In total, the four cities accounted for 4148 avoidable hospitalizations, which was associated to $1.1 million public health gains. Results considering the day of exposure (lag 0) were superior to those with the 5-day moving average (lag 5). The results highlighted the importance of adopting more restrictive standards and called for public policies, the necessity of expanding the air quality monitoring network, mapping emission sources, and improve the knowledge about the interaction between air pollution and health outcomes beyond respiratory disease for the region.

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM₁₀ and PM_2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM_2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM_2.5, were 32,000 ± 5,300 due to all-cause mortality, between 16,000 ± 2,100 and 51,000 ± 3,000 due non-accidental causes, between 7,300 ± 1,300 and 16,700 ± 1,500 due to cardiovascular disease, between 4,750 ± 900 and 10,950 ± 870 due ischemic heart diseases and 1,220 ± 330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM_2.5 among 'children' age group than for PM₁₀ (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840 ± 3,950 avoidable hospitalizations in the elderly related to a decrease in PM_2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.

Risk assessment of temperature and air pollutants on hospitalizations for mental and behavioral disorders in Curitiba, Brazil

BACKGROUND

Extreme ambient temperatures and air quality have been directly associated with various human diseases from several studies around the world. However, few analyses involving the association of these environmental circumstances with mental and behavioral disorders (MBD) have been carried out, especially in developing countries such as Brazil.

METHODS

A time series study was carried out to explore the associations between daily air pollutants (SO2, NO2, O3, and PM10) concentrations and meteorological variables (temperature and relative humidity) on hospital admissions for mental and behavioral disorders for Curitiba, Brazil. Daily hospital admissions from 2010 to 2016 were analyzed by a semi-parametric generalized additive model (GAM) combined with a distributed lag non-linear model (DLNM).

RESULTS

Significant associations between environmental conditions (10 μg/m3 increase in air pollutants and temperature °C) and hospitalizations by MBD were found. Air temperature was the environmental variable with the highest relative risk (RR) at 0-day lag for all ages and sexes analyzed, with RR values of 1.0182 (95% CI: 1.0009-1.0357) for men, and 1.0407 (95% CI: 1.0230-1.0587) for women. Ozone exposure was a risk for all women groups, being higher for the young group, with a RR of 1.0319 (95% CI: 1.0165-1.0483). Elderly from both sexes were more susceptible to temperature variability, with a RR of 1.0651 (95% CI: 1.0213-1.1117) for women, and 1.0215 (95% CI: 1.0195-1.0716) for men.

CONCLUSIONS

This study suggests that temperatures above and below the thermal comfort threshold, in addition to high concentrations of air pollutants, present significant risks on hospitalizations by MBD; besides, there are physiological and age differences resulting from the effect of this exposure.

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Background

Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.

Methods

Six machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models.

Results

The LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900–0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783–0.901), SVM (0.834, 95% CI: 0.774–0.894) and ANN (0.890, 95% CI: 0.836–0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879–0.974) and XGBoost (0.930, 95% CI: 0.878–0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.

Conclusion

This study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management.

Characteristics and temporal variations of organic and elemental carbon aerosols in PM1 in Changchun, Northeast China

The present study offers the first evaluation of organic and elemental carbon (OC and EC) of submicron (PM₁) fraction in Changchun (Northeast China) during a year-long sampling period (October 24, 2016 to October 23, 2017). More than 288 PM₁ (particulate matter with an aerodynamic diameter of less than 1 μm) samples were collected. The PM₁ concentrations ranged from 3.78 to 451.08 μg·m^-3, with an average of 57.73 μg·m^-3, which was 1.65 times higher than the Chinese National Standard II. Following the concept of the well-known IMPROVE algorithm, OC and EC values were obtained. The OC values ranged from 1.18 to 82.54 μg∙m^-3, and the EC values were from 0.30 to 14.19 μg∙m^-3. Total carbon (TC = EC + OC) constituted 9.11-40.35% of the total PM₁ mass, and OC dominated over EC. The average OC/EC ratio was 4.78, which implied a low percentage for vehicles and a high contribution of coal and biomass consumption to PM₁. Among OC, the annual primary organic carbon (POC) value was 7.69 μg∙m^-3, accounting for 63% of the OC, while secondary organic carbon (SOC) contributed 37% with 4.12 μg∙m^-3. Among EC, CHAR (EC1) dominated over SOOT (EC2 + EC3), and the CHAR/SOOR ratio ranged from 2.91 to 28.55. The results of the OC and EC values as well as the OC/EC and CHAR/SOOT ratios suggest that possible sources of PM₁ include vehicles, coal burning, cooking, and biomass burning.

Ambient air pollution and daily hospital admissions for respiratory system-related diseases in a heavy polluted city in Northeast China

Respiratory disease admission has been increasing in the recent 5 years due to heavy air pollutions and bad weather conditions in China. We investigated the short-term association of ambient air pollution with daily counts of hospital admissions due to respiratory infection diseases with stratified analysis by age (0-18, 19-65, and > 65 years old), gender (male, female), season (spring, summer, autumn, winter), and disease type (lung infections, asthma, COPD (chronic obstructive disease), URI (upper respiratory infections)) in heavy polluted city of Shenyang in China. Daily ambient air pollution concentrations, weather conditions, and hospital admission counts for 53 months (from November 1, 2013 to March 25, 2018) were extracted from related authorities in electronic databases. Associations between outdoor air pollution levels and hospital admissions were estimated for time lags of 0-7 days using quasi-Poisson additive regression models, adjusted for meteorological variables, holidays, day of week, and season, as well as eliminating autocorrelations. Single pollutant analysis results showed lung infection diseases were related to all pollutant concentration change with no lag effects. After adjusting for other pollutants and confounding factors, we found NO₂ was associated with daily admissions of lung infections (ER = 6.75%, 95% CI 1.24, 12.55), asthma (ER = 20.36%, 95% CI 4.26, 38.95; lag day 5, ER = 18.48%, 95% CI 2.83, 36.51), and COPD (ER = 13.27%, 95% CI 0.46, 27.71); CO was associated with lung infections and asthma with lag effects on lag days 1 and 4; and PM_2.5 was associated with COPD admissions on lag day 6. Respiratory hospital admissions in female over 65 years old and autumn were more associated with increased air pollutant levels. Our study results might add more detail evidences for relationship studies between air pollution exposure and respiratory diseases and contribute to the precise respiratory disease prevention and air pollution control strategies.

Association of Long-Term Exposure to Fine Particulate Matter and Cardio-Metabolic Diseases in Low- and Middle-Income Countries: A Systematic Review

: Background: Numerous epidemiological studies indicated high levels of particulate matter less than2.5 μm diameter (PM2.5) as a major cardiovascular risk factor. Most of the studies have been conducted in high-income countries (HICs), where average levels of PM2.5 are far less compared to low- and middle- income countries (LMICs), and their socio-economic profile, disease burden, and PM speciation/composition are very different. We systematically reviewed the association of long-term exposure to PM2.5 and cardio-metabolic diseases (CMDs) in LMICs.

METHODS

Multiple databases were searched for English articles with date limits until March 2018. We included studies investigating the association of long-term exposure to PM2.5 (defined as an annual average/average measure for 3 more days of PM2.5 exposure) and CMDs, such as hospital admissions, prevalence, and deaths due to CMDs, conducted in LMICs as defined by World Bank. We excluded studies which employed exposure proxy measures, studies among specific occupational groups, and specific episodes of air pollution.

RESULTS

A total of 5567 unique articles were identified, of which only 17 articles were included for final review, and these studies were from Brazil, Bulgaria, China, India, and Mexico. Outcome assessed were hypertension, type 2 diabetes mellitus and insulin resistance, and cardiovascular disease (CVD)-related emergency room visits/admissions, death, and mortality. Largely a positive association between exposure to PM2.5 and CMDs was found, and CVD mortality with effect estimates ranging from 0.24% to 6.11% increased per 10 μg/m3 in PM2.5. CVD-related hospitalizations and emergency room visits increased by 0.3% to 19.6%. Risk factors like hypertension had an odds ratio of 1.14, and type 2 diabetes mellitus had an odds ratio ranging from 1.14-1.32. Diversity of exposure assessment and health outcomes limited the ability to perform a meta-analysis.

CONCLUSION

Limited evidence on the association of long-term exposure to PM2.5 and CMDs in the LMICs context warrants cohort studies to establish the association.

Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis

BACKGROUND

Fine particulate matter (Particulate matter with diameter ≤ 2.5 μm) is associated with multiple health outcomes, with varying effects across seasons and locations. It remains largely unknown that which components of PM_2.5 are most harmful to human health.

METHODS

We systematically searched all the relevent studies published before August 1, 2018, on the associations of fine particulate matter constituents with mortality and morbidity, using Web of Science, MEDLINE, PubMed and EMBASE. Studies were included if they explored the associations between short term or long term exposure of fine particulate matter constituents and natural, cardiovascular or respiratory health endpoints. The criteria for the risk of bias was adapted from OHAT and New Castle Ottawa. We applied a random-effects model to derive the risk estimates for each constituent. We performed main analyses restricted to studies which adjusted the PM_2.5 mass in their models.

RESULTS

Significant associations were observed between several PM_2.5 constituents and different health endpoints. Among them, black carbon and organic carbon were most robustly and consistently associated with all natural, cardiovascular mortality and morbidity. Other potential toxic constituents including nitrate, sulfate, Zinc, silicon, iron, nickel, vanadium, and potassium were associated with adverse cardiovascular health, while nitrate, sulfate and vanadium were relevant for adverse respiratory health outcomes.

CONCLUSIONS

Our analysis suggests that black carbon and organic carbon are important detrimental components of PM_2.5, while other constituents are probably hazardous to human health. However, more studies are needed to further confirm our results.

[Influence of vehicular traffic density on hospital admissions due to respiratory tract cancer in the city of São Paulo, Brazil]

Pollution related to traffic is a major problem in urban centers and a large portion of the population is vulnerable to its health effects. This study sought to identify a potential association between hospital admissions due to respiratory tract cancer and vehicular traffic density in the city of São Paulo, Brazil. It is an ecological study of the public (Hospital Inpatient Authorization - AIH, in Portuguese) and private (Hospital Inpatient Communication - CIH, in Portuguese) health care systems, from 2004 to 2006, geocoded by individuals' residential addresses. Using a Besag-York-Mollié ecological model, we initially evaluated the relationship between number of cases of hospital admission due to respiratory tract cancer in each weighting area and the standardized co-variables: traffic density and Municipal Human Development Index (MHDI) as indicator of socioeconomic status. Using a classic Poisson model, we then evaluated the risk associated with growing traffic density categories. The Besag-York-Mollié model estimated a RR = 1.09 (95%CI: 1.02-1.15) and RR = 1.19 (95%CI: 1.10-1.29) of admission due to respiratory tract cancer for each increase of one standard deviation of traffic and MHDI, respectively. The Poisson model also showed a clear exposure-response gradient for admission due to respiratory tract cancer (IRR = 1.11; 95%CI: 1.07-1.15, for each 10 units of added traffic density). This study suggests that there is an association between residing in areas with high traffic density and hospital admissions due to respiratory tract cancer in the city of São Paulo.

Effect of Air Pollution and Hospital Admission: A Systematic Review

IntroductionMany epidemiological studies have demonstrated associations between air pollution levels and human health in terms of hospital admissions. The aim of this paper is to gather evidence concerning air pollution effects on the risk of hospital admission. We hypothesised that increase in: particulate matter (PM), ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulphur dioxide (SO2) levels would be associated with the increasing trend of hospital admission.MethodsA systematic review of literature was carried out. Literature search was done in Sage, Ovid Medline, Science Direct, Wiley and ProQuest from 2010 to 2016 using keywords "hospital admission and air pollution". Studies of any relevant design were included if they presented original data, included at least one analysis where hospital admission was the specific outcome, and one or more of the following exposures were investigated: PM, O3, CO, NO2 and SO2.ResultsA total of 175 potential studies were identified by the search. Twenty two studies qualified for the review. Air pollution was noted to have an excessive risk of 3.46 (95%CI, 1.67, 5.27) of total hospital admissions. Cardiovascular admission was noted to have an increased risk of hospitalization for PM2.5 of 1.5 to 2.0; PM10 (1.007 to 2.7); NO2 (1.04 to 1.17) and SO2 (1.007). For respiratory admission, PM2.5 can caused an increased risk of hospitalization by 1.1 to 1.8; PM10 (1.007 to 1.13); NO2 (1.08 to 1.94) and SO2 (1.02). While O3 have minimal effect on COPD and stroke, CO does not influence in the effect of these hospitalization.ConclusionThe exposure to air pollutants confers an increased risk of admission of several disease. Our findings call for greater awareness of environmental protection and the implementation of effective measures to improve the quality of air, which may reduce the risks of adverse effects on the population's health.

Effect of Air Pollution and Hospital Admission: A Systematic Review

Introduction:

Many epidemiological studies have demonstrated associations between air pollution levels and human health in terms of hospital admissions. The aim of this paper is to gather evidence concerning air pollution effects on the risk of hospital admission. We hypothesised that increase in: particulate matter (PM), ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), and sulphur dioxide (SO₂) levels would be associated with the increasing trend of hospital admission.

Methods:

A systematic review of literature was carried out. Literature search was done in Sage, Ovid Medline, Science Direct, Wiley, and ProQuest from 2010 to 2016 using keywords “hospital admission and air pollution”. Studies of any relevant design were included if they presented original data, included at least one analysis where hospital admission was the specific outcome, and one or more of the following exposures were investigated: PM, O₃, CO, NO₂ and SO₂.

Results:

A total of 175 potential studies were identified by the search. Twenty-two studies qualified for the review. Air pollution was noted to have an excessive risk of 3.46 (95% CI, 1.67, 5.27) of total hospital admissions. Cardiovascular admission was noted to have an increased risk of hospitalization for PM_2.5 of 1.5 to 2.0; PM₁₀ (1.007 to 2.7); NO₂ (1.04 to 1.17) and SO₂ (1.007). For respiratory admission, PM_2.5 can caused an increased risk of hospitalization by 1.1 to 1.8; PM₁₀ (1.007 to 1.13); NO₂ (1.08 to 1.94) and SO₂ (1.02). While O₃ have minimal effect on COPD and stroke, CO does not influence in the effect of these hospitalization.

Conclusion:

The exposure to air pollutants confers an increased risk of admission of several disease. Our findings call for greater awareness of environmental protection and the implementation of effective measures to improve the quality of air, which may reduce the risks of adverse effects on the population’s health.

Vascular and cardiac autonomic function and PM2.5 constituents among the elderly: A longitudinal study

BACKGROUND

Although epidemiologic studies have shown an association between the total mass of particulate matter <2.5μm in aerodynamic diameter (PM_2.5) and cardiovascular disease, few studies have examined PM_2.5 constituents associated with vascular and cardiac autonomic dysfunction.

METHODS

In this longitudinal study, we investigated the relationship between PM_2.5 constituents and blood pressure (BP), and markers of the autonomic nervous system. In 466 elderly subjects residing in communities in Seoul, Korea, we examined 16 constituents, seven sources, and total mass concentrations of PM_2.5. We measured the BP, heart rate (HR), and indices of heart rate variability (HRV), such as the standard deviation of normal-to-normal intervals (SDNN), square root of the mean squared differences of successive NN intervals (rMSSD), and two frequency-domain variables (low frequency [LF] and high frequency [HF]). We used linear mixed effects models to assess the association of PM_2.5 constituents and sources with cardiovascular markers.

RESULTS

BP, HR, and rMSSD were associated with concentration of total mass of PM_2.5. For each increase of the interquartile range in PM_2.5 constituents, systolic and diastolic BP, and HR increased by 2.1-3.3mmHg, 1.2-2.3mmHg, and 1.2-1.9bpm, respectively, while the rMSSD, LF, and HF decreased by 8.1-9.3%, 16.6%, and 20.4%, respectively. Particularly, elemental carbon, sulfate, ammonium, lead, and strontium in the PM_2.5 constituents and emissions from oil combustion and incineration were associated with increased BP, HR, and decreased HRV.

CONCLUSIONS

Our results suggest an association between specific PM_2.5 constituents and vascular and cardiac autonomic functions. These findings may provide supportive evidence for developing a pollution reduction plan to prevent cardiovascular diseases.

Evaluation of the Effects of Airborne Particulate Matter on Bone Marrow-Mesenchymal Stem Cells (BM-MSCs): Cellular, Molecular and Systems Biological Approaches

Particulate matter (PM) contains heavy metals that affect various cellular functions and gene expression associated with a range of acute and chronic diseases in humans. However, the specific effects they exert on the stem cells remain unclear. Here, we report the effects of PM collected from the city of Jeddah on proliferation, cell death, related gene expression and systems of biological analysis in bone marrow mesenchymal stem cells (BM-MSCs), with the aim of understanding the underlying mechanisms. PM_2.5 and PM₁₀ were tested in vitro at various concentrations (15 to 300 µg/mL) and durations (24 to 72 h). PMs induced cellular stress including membrane damage, shrinkage and death. Lower concentrations of PM_2.5 increased proliferation of BM-MSCs, while higher concentrations served to decrease it. PM₁₀ decreased BM-MSCs proliferation in a concentration-dependent manner. The X-ray fluorescence spectrometric analysis showed that PM contains high levels of heavy metals. Ingenuity Pathway Analysis (IPA) and hierarchical clustering analyses demonstrated that heavy metals were associated with signaling pathways involving cell stress/death, cancer and chronic diseases. qRT-PCR results showed differential expression of the apoptosis genes (BCL2, BAX); inflammation associated genes (TNF-α and IL-6) and the cell cycle regulation gene (p53). We conclude that PM causes inflammation and cell death, and thereby predisposes to chronic debilitating diseases.