Concept: National Ambient Air Quality Standards
Policymakers around the world are turning to license-plate based driving restrictions in an effort to address urban air pollution. The format differs across cities, but most programs restrict driving once or twice a week during weekdays. This paper focuses on Mexico City, home to one of the oldest and best-known driving restriction policies. For almost two decades Mexico City’s driving restrictions applied during weekdays only. This changed recently, however, when the program was expanded to include Saturdays. This paper uses hourly data from pollution monitoring stations to measure the effect of the Saturday expansion on air quality. Overall, there is little evidence that the program expansion improved air quality. Across eight major pollutants, the program expansion had virtually no discernible effect on pollution levels. These disappointing results stand in sharp contrast to estimates made before the expansion which predicted a 15%+ decrease in vehicle emissions on Saturdays. To understand why the program has been less effective than expected, the paper then turns to evidence from subway, bus, and light rail ridership, finding no evidence that the expansion was successful in getting drivers to switch to lower-emitting forms of transportation.
Background Studies have shown that long-term exposure to air pollution increases mortality. However, evidence is limited for air-pollution levels below the most recent National Ambient Air Quality Standards. Previous studies involved predominantly urban populations and did not have the statistical power to estimate the health effects in underrepresented groups. Methods We constructed an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up. Annual averages of fine particulate matter (particles with a mass median aerodynamic diameter of less than 2.5 μm [PM2.5]) and ozone were estimated according to the ZIP Code of residence for each enrollee with the use of previously validated prediction models. We estimated the risk of death associated with exposure to increases of 10 μg per cubic meter for PM2.5 and 10 parts per billion (ppb) for ozone using a two-pollutant Cox proportional-hazards model that controlled for demographic characteristics, Medicaid eligibility, and area-level covariates. Results Increases of 10 μg per cubic meter in PM2.5 and of 10 ppb in ozone were associated with increases in all-cause mortality of 7.3% (95% confidence interval [CI], 7.1 to 7.5) and 1.1% (95% CI, 1.0 to 1.2), respectively. When the analysis was restricted to person-years with exposure to PM2.5 of less than 12 μg per cubic meter and ozone of less than 50 ppb, the same increases in PM2.5 and ozone were associated with increases in the risk of death of 13.6% (95% CI, 13.1 to 14.1) and 1.0% (95% CI, 0.9 to 1.1), respectively. For PM2.5, the risk of death among men, blacks, and people with Medicaid eligibility was higher than that in the rest of the population. Conclusions In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM2.5 and ozone at concentrations below current national standards. This effect was most pronounced among self-identified racial minorities and people with low income. (Supported by the Health Effects Institute and others.).
Exposure to ambient air pollutants has been associated with increased lung cancer incidence and mortality, but due to the high case fatality rate, little is known about the impacts of air pollution exposures on survival after diagnosis. This study aimed to determine whether ambient air pollutant exposures are associated with the survival of patients with lung cancer.
Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)
- International journal of environmental research and public health
- Published almost 5 years ago
A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area.
Airborne compounds in the indoor environment arise from a wide variety of sources such as environmental tobacco smoke, heating and cooking, construction materials as well as outdoor sources. To understand the contribution of scented candles to the indoor load of airborne substances and particulate matter, candle emission testing was undertaken in environmentally controlled small and large emission chambers. Candle emission rates, calculated on the basis of measured chamber concentrations of volatile and semi volatile organic compounds (VOC, SVOC) and particulate matter (PM), were used to predict their respective indoor air concentrations in a standard EU-based dwelling using 2 models: the widely accepted ConsExpo 1-box inhalation model and the recently developed RIFM 2-box indoor air dispersion model. The output from both models has been used to estimate more realistic consumer exposure concentrations of specific chemicals and PM in candle emissions. Potential consumer health risks associated with the candle emissions were characterized by comparing the exposure concentrations with existing indoor or ambient air quality guidelines or, where not existent, to established toxicity thresholds. On the basis of this investigation it was concluded that under normal conditions of use scented candles do not pose known health risks to the consumer.
- Bulletin of environmental contamination and toxicology
- Published over 3 years ago
Multivariate statistical techniques were employed on twelve leaf traits in four selected common tree species (Mangifera indica L., Polyalthia longifolia Sonn., Ficus benghalensis L. and Psidium guajava L.) to evaluate their responses with respect to major air pollutants in an urban area. Discriminant analysis (DA) identified chlorophyll/carotenoid ratio, leaf dry matter content, carotenoids, net water content and ascorbic acid as the major discriminating leaf traits, which varied maximally with respect to the pollution status. Pollution response score (PRS), calculated on the basis of discriminate functional coefficient values, increased with an increase in air pollution variables for all the tested species, with the highest increase in P. longifolia and the lowest in F. benghalensis. The study highlights the usefulness of DA for evaluation of plant specific traits and PRS for selection of tolerant species.
Air pollution has been associated with increased mortality and morbidity in several studies with indications that its effect could be more severe in children. This study examined the relationship between short-term variations in criteria air pollutants and occurrence of sudden infant death syndrome (SIDS).
Ambient air pollutants may increase preterm birth (PTB) risk, but critical exposure windows are uncertain. The interaction of asthma and pollutant exposure is rarely studied.
The adverse effects of traffic-related air pollution on children’s respiratory health have been widely reported, but few studies have evaluated the impact of traffic-control policies designed to reduce urban air pollution. We assessed associations between traffic-related air pollutants and respiratory/allergic symptoms amongst 8-9 year-old schoolchildren living within the London Low Emission Zone (LEZ). Information on respiratory/allergic symptoms was obtained using a parent-completed questionnaire and linked to modelled annual air pollutant concentrations based on the residential address of each child, using a multivariable mixed effects logistic regression analysis. Exposure to traffic-related air pollutants was associated with current rhinitis: NOx (OR 1.01, 95% CI 1.00-1.02), NO2 (1.03, 1.00-1.06), PM10 (1.16, 1.04-1.28) and PM2.5 (1.38, 1.08-1.78), all per μg/m3 of pollutant, but not with other respiratory/allergic symptoms. The LEZ did not reduce ambient air pollution levels, or affect the prevalence of respiratory/allergic symptoms over the period studied. These data confirm the previous association between traffic-related air pollutant exposures and symptoms of current rhinitis. Importantly, the London LEZ has not significantly improved air quality within the city, or the respiratory health of the resident population in its first three years of operation. This highlights the need for more robust measures to reduce traffic emissions.
Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China’s Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.