Air pollutant concentrations

About: Air pollutant concentrations is a research topic. Over the lifetime, 1652 publications have been published within this topic receiving 36138 citations.

Using Low Cost Sensors to Measure Ambient Particulate Matter Concentrations and On-Road Emissions Factors

Abstract: . Air quality is a growing public concern in both developed and developing countries, as is the public interest in having information on air pollutant concentrations within their communities. Quantifying the spatial and temporal variability of ambient fine particulate matter (PM2.5) is of particular importance due to the well-defined health impacts associated with PM2.5. This work evaluates a number of select PM sensors (Shinyei: models PPD42NS, PPD20V, PPD60PV) under a variety of ambient conditions and locations including urban background and roadside sites in Atlanta, GA, as well as a location with substantially higher ambient concentrations in Hyderabad, India. Low cost sensor measurements were compared against reference monitors at all locations. On-road emissions factors were calculated at the Atlanta site by pairing PM2.5 and separately determined black carbon (BC) and carbon dioxide (CO2) measurements. On-road emission factors can vary in different locations and over time for a number of reasons, including vehicle fleet composition and driving patterns and behaviors, and current environmental policy. Emission factors can provide valuable information to inform researchers, citizens, and policy makers. The PPD20V sensors had the highest correlation with the reference environmental beta attenuation monitor (E-BAM) with R2 values above 0.80 at the India site while at the urban background site, the PPD60PV had the highest correlation with the tapered element oscillating microbalance (TEOM) with an R2 value of 0.30. At the roadside site, only the PPD20V was used, with an R2 value against the TEOM of 0.18. Emissions factors at the roadside site were calculated as 0.39 ± 0.10 g PM2.5 per kg fuel and 0.11 ± 0.01g BC per kg fuel, which compare well with other studies and estimates based on other instruments. The results of this work show the potential usefulness of these sensors for high concentration applications in developing countries and for their use in generating emissions factors.

The impact of the congestion charging scheme on air quality in London. Part 1. Emissions modeling and analysis of air pollution measurements.

Abstract: On February 17, 2003, a congestion charging scheme (CCS*) was introduced in central London along with a program of traffic management measures. The scheme operated Monday through Friday, 7 AM to 6 PM. This program resulted in an 18% reduction in traffic volume and a 30% reduction in traffic congestion in the first year (2003). We developed methods to evaluate the possible effects of the scheme on air quality: We used a temporal-spatial design in which modeled and measured air quality data from roadside and background monitoring stations were used to compare time periods before (2001-2002) and after (2003-2004) the CCS was introduced and to compare the spatial area of the congestion charging zone (CCZ) with the rest of London. In the first part of this project, we modeled changes in concentrations of oxides of nitrogen (NOx), nitrogen dioxide (NO2), and PM10 (particles with a mass median aerodynamic diameter < or = 10 microm) across the CCZ and in Greater London under different traffic and emission scenarios for the periods before and after CCS introduction. Comparing model results within and outside the zone suggested that introducing the CCS would be associated with a net 0.8-microg/m3 decrease in the mean concentration of PM10 and a net 1.7-ppb decrease in the mean concentration of NOx within the CCZ. In contrast, a net 0.3-ppb increase in the mean concentration of NO2 was predicted within the zone; this was partly explained by an expected increase in primary NO2 emissions due to the introduction of particle traps on diesel buses (one part of the improvements in public transport associated with the CCS). In the second part of the project, we established a CCS Study Database from measurements obtained from the London Air Quality Network (LAQN) for air pollution monitors sited to measure roadside and urban background concentrations. Fully ratified (validated) 15-minute mean carbon monoxide (CO), nitric oxide (NO), NO2, NOx, PM10, and PM2.5 data from each chosen monitoring site for the period from February 17, 2001, to February 16, 2005, were transferred from the LAQN database. In the third part of our project, these data were used to compare geometric means for the 2 years before and the 2 years after the CCS was introduced. Temporal changes within the CCZ were compared with changes, over the same period, at similarly sited (roadside or background) monitors in a control area 8 km distant from the center of the CCZ. The analysis was confined to measurements obtained during the hours and days on which the scheme was in operation and focused on pollutants derived from vehicles (NO, NO2, NOx, PM10, and CO). This set of analyses was based on the limited data available from within the CCZ. When compared with data from outside the zone, we did not find evidence of temporal changes in roadside measurements of NOx, NO, and NO2, nor in urban background concentrations of NOx. (The latter result, however, concealed divergent trends in NO, which fell, and NO2, which rose.) Although based upon fewer stations, there was evidence that background concentrations of PM10 and CO fell within the CCZ compared with outside the zone. We also analyzed the trends in background concentrations for all London monitoring stations; as distance from the center of the CCZ increased, we found some evidence of an increasing gradation in NO and PM10 concentrations before versus after the intervention. This suggests a possible intermediate effect on air quality in the area immediately surrounding the CCZ. Although London is relatively well served with air quality monitoring stations, our study was restricted by the availability of only a few monitoring sites within the CCZ, and only one of those was at a roadside location. The results derived from this single roadside site are not likely to be an adequate basis for evaluating this complex urban traffic management scheme. Our primary approach to assessing the impact of the CCS was to analyze the changes in geometric mean pollutant concentrations in the 2 years before and 2 years after the CCS was introduced and to compare changes at monitoring stations within the CCZ with those in a distant control area (8 km from the CCZ center) unlikely to be influenced by the CCS. We saw this as the most robust analytical approach with which to examine the CCS Study Database, but in the fourth part of the project we did consider three other approaches: ethane as an indicator of pollution dispersion; the cumulative sum (CUSUM) statistical technique; and bivariate polar plots for local emissions. All three were subsequently judged as requiring further development outside of the scope of this study. However, despite their investigative nature, each technique provided useful information supporting the main analyses. The first method used ethane as a dispersion indicator to remove the inherent variability in air pollutant concentrations caused by changes in meteorology and atmospheric dispersion. The technique had the potential to ascertain more accurately the likely impacts of the CCS on London's air quality. Although this novel method appeared promising over short time periods, a number of concerns arose about whether the spatial and temporal variability of ethane over longer time periods would be representative of meteorologic conditions alone. The major strength of CUSUM, the second method, is that it can be used to identify the approximate timing of changes that may have been caused by the CCS. This ability is weakened, however, by the effects of serial correlation (the correlation of data among measurements in successive time intervals) within air pollution data that is caused by seasonality and long-term meteorologic trends. The secure interpretation of CUSUM requires that the technique be adapted to take proper account of the underlying correlation between measurements without the use of smoothing functions that would obscure a stepped change in concentrations. Although CUSUM was not able to provide a quantitative estimation of changes in pollution levels arising from the introduction of the CCS, the strong signals that were identified were considered in the context of other results from the study. The third method, bivariate polar plots, proved useful. The plots revealed important characteristics of the data from the only roadside monitoring site within the CCZ and highlighted the importance of considering prevailing weather conditions when positioning a roadside monitor. The technique would benefit from further development, however, in transforming the qualitative assessment of change into a quantitative assessment and including an estimate of uncertainty. Research is ongoing to develop this method in air-quality time-series studies. Overall, using a range of measurement and modeling approaches, we found evidence of small changes in air quality after introduction of the CCS. These include small decreases in PM10, NO, and CO. The possibility that some of these effects might reflect more general changes in London's air quality is suggested by the findings of somewhat similar changes in geometric means for weekends, when the CCS was not operating. However, since some evidence suggests that the CCS also had an impact on traffic volume on weekends, the CCS remains as one possible explanation for the observed pattern of changes in pollutant concentrations. In addition, the CCS was just one of a number of traffic and emission reduction schemes introduced in London over the 4-year study period; if the other measures had an impact in central London, they might partly explain our findings. Although not the aim of this study, it is important to consider how the trends we observed might be translated into health effects. For example, given that London already has NO2 concentrations in excess of the permitted limit value, we do not know what the effects of an increase in NO2 created by diesel-exhaust after-treatment for particles might mean for health. Further, although it is not likely that NO affects health, the decrease in NO concentrations is likely associated with an increase in ozone concentrations (a pollutant associated with health effects), as has been seen in recent years in London. These and other similar issues require further investigation. Although the CCS is a relatively simple traffic management scheme in the middle of a major urban environment, analyzing its possible impact on air quality was found to be far from straightforward. Using a range of modeling and monitoring approaches to address the impact of the scheme revealed that each technique has its own advantages and limitations. The placement of monitoring sites and the availably of traffic count data were also identified as key issues. The most compelling lesson we take away from this study is that such work is impossible to undertake without a coherent multi-disciplinary team of skilled researchers. In conclusion, our study suggests that the introduction of the CCS in 2003 was associated with small temporal changes in air pollutant concentrations in central London compared with outer areas. However, attributing the cause of these changes to the CCS alone is not appropriate because the scheme was introduced at a time when other traffic and emissions interventions, which might have had a more concentrated effect in central London, were also being implemented.

The Impact of Ambient Air Pollution on Daily Hospital Visits for Various Respiratory Diseases and the Relevant Medical Expenditures in Shanghai, China.

Abstract: The evidence concerning the acute effects of ambient air pollution on various respiratory diseases was limited in China, and the attributable medical expenditures were largely unknown. From 2013 to 2015, we collected data on the daily visits to the emergency- and outpatient-department for five main respiratory diseases and their medical expenditures in Shanghai, China. We used the overdispersed generalized additive model together with distributed lag models to fit the associations of criteria air pollutants with hospital visits, and used the linear models to fit the associations with medical expenditures. Generally, we observed significant increments in emergency visits (8.81–17.26%) and corresponding expenditures (0.33–25.81%) for pediatric respiratory diseases, upper respiratory infection (URI), and chronic obstructive pulmonary disease (COPD) for an interquartile range increase of air pollutant concentrations over four lag days. As a comparison, there were significant but smaller increments in outpatient visits (1.36–4.52%) and expenditures (1.38–3.18%) for pediatric respiratory diseases and upper respiratory infection (URI). No meaningful changes were observed for asthma and lower respiratory infection. Our study suggested that short-term exposure to outdoor air pollution may induce the occurrences or exacerbation of pediatric respiratory diseases, URI, and COPD, leading to considerable medical expenditures upon the patients.

Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran

Abstract: Ambient air pollution, is one of the most frequently stated environmental problems. Many epidemiological studies have documented adverse health effects for ambient air pollution. This study aimed to investigate the association between ambient air pollution and respiratory hospital admissions. In this ecological time series study data about air pollutant concentrations including CO, NO2, O3, PM2.5, PM10 and SO2 and, respiratory hospital admissions in the urban population of Arak, from January 1st 2010 to December 31st 2015; were inquired, from the Arak Department of Environment, and two major hospitals, respectively. Meteorological data were inquired for the same period as well. Time-series regression analysis with a distributed lag model, controlled for seasonality long-time trends, weather and day of the week, was used for data analysis. Every 10 μg/m3 increase in NO2, and PM10 and every 1 mg/m3 increase in CO at lag 0 corresponded to a RR = 1.032 (95%CI, 1.003–1.06), RR = 1.01 (95%CI, 1.004–1.017) and RR = 1.09 (95%CI, 1.04–1.14), increase in respiratory disease hospitalizations, respectively. Males and the elderly were found to be more susceptible than females and other age groups to air pollutants in regard to respiratory disease admissions. The results of this study showed that outdoor air pollutants significantly increase respiratory hospital admissions; especially among the men and elders in Arak.

Air quality model simulating photochemical formation of pollutants: the Sao Paulo Metropolitan Area, Brazil

Abstract: The main sources of ozone precursors in the atmosphere of the Sao Paulo Metropolitan Area (SPMA) in Brazil are emissions from gasoline-, ethanol-, and diesel-powered vehicles. Ozone is a significant air quality problem in the SPMA. To come into compliance with the National Ambient Air quality Standard for Ozone, emission reduction policies must be established. An adequate emissions inventory and a description of the meteorology and chemistry involved are needed in order to evaluate the effectiveness of methodologies that reduce the effect of vehicle emissions on ozone formation. During the period 10–12 August 1999, concentrations of ozone in the SPMA and the impact of the official emissions inventory were simulated through application of an urban-scale Eulerian model. We found that using the official inventory in simulations resulted in ozone values much lower than those observed and nitrogen oxide emission profiles that were overestimated by a factor of approximately two.