Developing a smartphone software package for predicting atmospheric pollutant concentrations at mobile locations.
ABSTRACT: There is considerable evidence that exposure to air pollution is harmful to health. In the U.S., ambient air quality is monitored by Federal and State agencies for regulatory purposes. There are limited options, however, for people to access this data in real-time which hinders an individual's ability to manage their own risks. This paper describes a new software package that models environmental concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM10), and ozone concentrations for the state of Oregon and calculates personal health risks at the smartphone's current location. Predicted air pollution risk levels can be displayed on mobile devices as interactive maps and graphs color-coded to coincide with EPA air quality index (AQI) categories. Users have the option of setting air quality warning levels via color-coded bars and were notified whenever warning levels were exceeded by predicted levels within 10 km. We validated the software using data from participants as well as from simulations which showed that the application was capable of identifying spatial and temporal air quality trends. This unique application provides a potential low-cost technology for reducing personal exposure to air pollution which can improve quality of life particularly for people with health conditions, such as asthma, that make them more susceptible to these hazards.
Project description:BACKGROUND:To mitigate air pollution-related health risks and target interventions towards the populations bearing the greatest risks, the City Health Outlook (CHO) project aims to establish multi-scale, long-lasting, real-time urban environment and health monitoring networks. A major goal of CHO is to collect data of personal exposure to particulate air pollution through a full profile that consists of a matrix of activities and micro-environments. As the first paper of a series, this paper is targeted at illustrating the characteristics of the participants and examining the effects of different covariates on personal exposure at various air pollution exposure levels. METHODS:In the first campaign, volunteers are recruited to wear portable environmental sensors to record their real-time personal air pollution exposure and routes. After a web-based social media recruitment strategy, 50 eligible subjects joined the first campaign in Beijing from January 8 to January 20, 2018. The mean personal exposures were measured at 19.36, 37.65, and 43.45 μg/m3 for particulate matter (PM) with a diameter less than 1, 2.5, and 10 μm, respectively, albeit with the high spatial-temporal variations. RESULTS:Unequal distribution of exposures was observed in the subjects with different sociodemographic status, travel behavior, living and health conditions. Quantile regression analysis reveals that subjects who are younger, less educated, exposed to passive smoking, low to middle household income, overweight, without ventilation system at home or office, and do not possess private vehicles, are more susceptible to PM pollution. The differences, however, are generally insignificant at low exposure levels and become evident on bad air quality days. CONCLUSIONS:The heterogeneity in personal exposure found in this the first CHO campaign highlighted the importance of studying the pollution exposure at the individual scale. It is at the critical stage to bridge the knowledge gap of environmental inequality in different populations, which can lead to great health implications.
Project description:As global awareness of air pollution rises, so does the imperative to provide evidence-based recommendations for strategies to mitigate its impact. While public policy has a central role in reducing air pollution, exposure can also be reduced by personal choices. Qualified evidence supports limiting physical exertion outdoors on high air pollution days and near air pollution sources, reducing near-roadway exposure while commuting, utilising air quality alert systems to plan activities, and wearing facemasks in prescribed circumstances. Other strategies include avoiding cooking with solid fuels, ventilating and isolating cooking areas, and using portable air cleaners fitted with high-efficiency particulate air filters. We detail recommendations to assist providers and public health officials when advising patients and the public regarding personal-level strategies to mitigate risk imposed by air pollution, while recognising that well-designed prospective studies are urgently needed to better establish and validate interventions that benefit respiratory health in this context.
Project description:Household air pollution from biomass cookstoves is estimated to be responsible for more than two and a half million premature deaths annually, primarily in low and middle-income countries where cardiometabolic disorders, such as Type II Diabetes, are increasing. Growing evidence supports a link between ambient air pollution and diabetes, but evidence for household air pollution is limited. This cross-sectional study of 142 women (72 with traditional stoves and 70 with cleaner-burning Justa stoves) in rural Honduras evaluated the association of exposure to household air pollution (stove type, 24-hour average kitchen and personal fine particulate matter [PM2.5 ] mass and black carbon) with glycated hemoglobin (HbA1c) levels and diabetic status based on HbA1c levels. The prevalence ratio (PR) per interquartile range increase in pollution concentration indicated higher prevalence of prediabetes/diabetes (vs normal HbA1c) for all pollutant measures (eg, PR per 84 ?g/m3 increase in personal PM2.5 , 1.49; 95% confidence interval [CI], 1.11-2.01). Results for HbA1c as a continuous variable were generally in the hypothesized direction. These results provide some evidence linking household air pollution with the prevalence of prediabetes/diabetes, and, if confirmed, suggest that the global public health impact of household air pollution may be broader than currently estimated.
Project description:Indoor exposure to fine particulate matter (PM2.5) is a prominent health concern. However, few studies have examined the effectiveness of long-term use of indoor air filters for reduction of PM2.5 exposure and associated decrease in adverse health impacts in urban India. We conducted 20 simulations of yearlong personal exposure to PM2.5 in urban Delhi using the National Institute of Standards and Technology's CONTAM program (NIST, Gaithersburg, MD, USA). Simulation scenarios were developed to examine different air filter efficiencies, use schedules, and the influence of a smoker at home. We quantified associated mortality reductions with Household Air Pollution Intervention Tool (HAPIT, University of California, Berkeley, CA, USA). Without an air filter, we estimated an annual mean PM2.5 personal exposure of 103 µg/m3 (95% Confidence Interval (CI): 93, 112) and 137 µg/m3 (95% CI: 125, 149) for households without and with a smoker, respectively. All day use of a high-efficiency particle air (HEPA) filter would reduce personal PM2.5 exposure to 29 µg/m3 and 30 µg/m3, respectively. The reduced personal PM2.5 exposure from air filter use is associated with 8-37% reduction in mortality attributable to PM2.5 pollution in Delhi. The findings of this study indicate that air filter may provide significant improvements in indoor air quality and result in health benefits.
Project description:The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean R¯2 = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (mean R¯2 = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.
Project description:BACKGROUND: Green spaces are reported to improve health status, including beneficial effects on pregnancy outcomes. Despite the suggestions of air pollution-related health benefits of green spaces, there is no available evidence on the impact of greenness on personal exposure to air pollution. OBJECTIVES: We investigated the association between surrounding greenness and personal exposure to air pollution among pregnant women and to explore the potential mechanisms, if any, behind this association. METHODS: In total, 65 rounds of sampling were carried out for 54 pregnant women who resided in Barcelona during 2008-2009. Each round consisted of a 2-day measurement of particulate matter with aerodynamic diameter ? 2.5 ?m (PM?.?) and a 1-week measurement of nitric oxides collected simultaneously at both the personal and microenvironmental levels. The study participants were also asked to fill out a time-microenvironment-activity diary during the sampling period. We used satellite retrievals to determine the surrounding greenness as the average of Normalized Difference Vegetation Index (NDVI) in a buffer of 100 m around each maternal residential address. We estimated the impact of surrounding greenness on personal exposure levels, home-outdoor and home-indoor pollutant levels, and maternal time-activity. RESULTS: Higher residential surrounding greenness was associated with lower personal, home-indoor, and home-outdoor PM?.? levels, and more time spent at home-outdoor. CONCLUSIONS: We found lower levels of personal exposure to air pollution among pregnant women residing in greener areas. This finding may be partly explained by lower home-indoor pollutant levels and more time spent in less polluted home-outdoor environment by pregnant women in greener areas.
Project description:Few prospective studies have assessed the blood pressure effect of extremely high air pollution encountered in Asia's megacities. The objective of this study was to evaluate the association between combustion-related air pollution with ambulatory blood pressure and autonomic function. During February to July 2012, personal black carbon was determined for 5 consecutive days using microaethalometers in patients with metabolic syndrome in Beijing, China. Simultaneous ambient fine particulate matter concentration was obtained from the Beijing Municipal Environmental Monitoring Center and the US Embassy. Twenty-four-hour ambulatory blood pressure and heart rate variability were measured from day 4. Arterial stiffness and endothelial function were obtained at the end of day 5. For statistical analysis, we used generalized additive mixed models for repeated outcomes and generalized linear models for single/summary outcomes. Mean (SD) of personal black carbon and fine particulate matter during 24 hours was 4.66 (2.89) and 64.2 (36.9) ?g/m(3). Exposure to high levels of black carbon in the preceding hours was associated significantly with adverse cardiovascular responses. A unit increase in personal black carbon during the previous 10 hours was associated with an increase in systolic blood pressure of 0.53 mm Hg and diastolic blood pressure of 0.37 mm Hg (95% confidence interval, 0.17-0.89 and 0.10-0.65 mm Hg, respectively), a percentage change in low frequency to high frequency ratio of 5.11 and mean interbeat interval of -0.06 (95% confidence interval, 0.62-9.60 and -0.11 to -0.01, respectively). These findings highlight the public health effect of air pollution and the importance of reducing air pollution.
Project description:The nature of pollutants involved in smog episodes can vary significantly in various cities and contexts and will impact local populations differently due to actual exposure and pre-existing sensitivities for cardiovascular or respiratory diseases. While regulated standards and guidance remain important, it is relevant for cities to have local warning systems related to air pollution. The present paper proposes indicators and thresholds for an air pollution warning system in the metropolitan areas of Montreal and Quebec City (Canada). It takes into account past and current local health impacts to launch its public health warnings for short-term episodes. This warning system considers fine particulate matter (PM2.5) as well as the combined oxidant capacity of ozone and nitrogen dioxide (Ox) as environmental exposures. The methodology used to determine indicators and thresholds consists in identifying extreme excess mortality episodes in the data and then choosing the indicators and thresholds to optimize the detection of these episodes. The thresholds found for the summer were 31 μg/m3 for PM2.5 and 43 ppb for Ox in Montreal, and 32 μg/m3 and 23 ppb in Quebec City. In winter, thresholds found were 25 μg/m3 and 26 ppb in Montreal, and 33 μg/m3 and 21 ppb in Quebec City. These results are in line with different guidelines existing concerning air quality, but more adapted to the cities examined. In addition, a sensitivity analysis is conducted which suggests that Ox is more determinant than PM2.5 in detecting excess mortality episodes.
Project description:This investigation determined the effects of air pollution on childhood asthma hospitalization in regions with differing air pollution levels in Taiwan over a long time period. Data of childhood hospital admissions for asthma in patients aged 0?18 years and air quality in eight regions for the period 2001?2012 in Taiwan were collected. Poisson generalized linear regression analysis was employed to identify the relative risks of hospitalization due to asthma in children associated with exposure to varying levels of air pollutants with a change in the interquartile range after adjusting for temperature and relative humidity. Particulate matter ?2.5 ?m (PM2.5), particulate matter ?10 ?m (PM10), ozone (O?), sulfur dioxide (SO?), and nitrogen dioxide (NO?), were positively associated with childhood asthma hospitalization, while O? was negatively associated with childhood asthma hospitalization. SO? was identified as the most significant risk factor. The relative risks for asthma hospitalization associated with air pollutants were higher among children aged 0?5 years than aged 6?18 years and were higher among males than females. The effects of air pollution on childhood asthma were greater in the higher-level air pollution regions, while no association was observed in the lower-level air pollution regions. These findings may prove important for policymakers involved in implementing policies to reduce air pollution.
Project description:Although the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people's subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent's SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI.