Acute effect of daily fine particulate matter pollution on cerebrovascular mortality in Shanghai,China: a population-based time series study

Numerous studies have investigated the impacts of ambient fine particulate matter (PM_2.5) on human health. In this study, we examined the association of daily PM_2.5 concentrations with the number of deaths for the cerebrovascular disease on the same day, using the generalized additive model (GAM) controlling for temporal trend and meteorological variables. We used the data between 2012 and 2014 from Shanghai, China, where the adverse health effects of PM_2.5 have been of particular concern. Three different approaches (principal component analysis, shrinkage smoothers, and the least absolute shrinkage and selection operator regularization) were used in GAM to handle multicollinear meteorological variables. Our results indicate that the average daily concentration of PM_2.5 in Shanghai was high, 55 μg/m³, with an average daily death for cerebrovascular disease (CVD) of 62. There was 1.7% raised cerebrovascular disease deaths per 10 μg/m³ increase in PM_2.5 concentration in the unadjusted model. However, PM_2.5 concentration was no longer associated with CVD deaths after controlling for meteorological variables. The results were consistent in the three modelling techniques that we used. As a large number of people are exposed to air pollution, further investigation with longer time period including individual-level information is needed to examine the association.

     

Exploring relationships between outdoor air particulate-associated polycyclic aromatic hydrocarbon and PM2.5: A case study of benzo(a)pyrene in California metropolitan regions

Polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM) are co-pollutants emitted as by-products of combustion processes. Convincing evidence exists for PAHs as a primary toxic component of fine PM (PM_2.5). Because PM_2.5 is listed by the US EPA as a “Criteria Pollutant”, it is monitored regularly at sites nationwide. In contrast, very limited data is available on measured ambient air concentrations of PAHs. However, between 1999 and 2001, ambient air concentrations of PM_2.5 and benzo(a)pyrene (BaP) are available for California locations. We use multivariate linear regression models (MLRMs) to predict ambient air levels of BaP in four air basins based on reported PM_2.5 concentrations and spatial, temporal and meteorological variables as variates. We obtain an R² ranging from 0.57 to 0.72 among these basins. Significant variables (p<0.05) include the average daily PM_2.5 concentration, wind speed, temperature and relative humidity, and the coastal distance as well as season, and holiday or weekend. Combining the data from all sites and using only these variables to estimate ambient BaP levels, we obtain an R² of 0.55. These R²-values, combined with analysis of the residual error and cross validation using the PRESS-statistic, demonstrate the potential of our method to estimate reported outdoor air PAH exposure levels in metropolitan regions. These MLRMs provide a first step towards relating outdoor ambient PM_2.5 and PAH concentrations for epidemiological studies when PAH measurements are unavailable, or limited in spatial coverage, based on publicly available meteorological and PM_2.5 data.     

A combined model–observation approach to estimate historic gridded fields of PM2.5 mass and species concentrations

This paper introduces a methodology for estimating gridded fields of total and speciated fine particulate matter (PM_2.5) concentrations for time periods and regions not covered by observational data. The methodology is based on performing long-term regional scale meteorological and air quality simulations and then integrating these simulations with available observational data. To illustrate this methodology, we present an application in which year-round simulations with a meteorological model (the National Center for Atmospheric Research/Penn State Mesoscale Model, hereafter referred to as MM5) and a photochemical air quality model (the Community Multiscale Air Quality Model, hereafter referred to as CMAQ) have been performed over the northeastern United States for 1988–2005. Model evaluation results for total PM_2.5 mass and individual species for the time period from 2000 to 2005 show that model performance varies by species, season, and location. Therefore, an approach is developed to adjust CMAQ output with factors based on these three variables. The adjusted model values for total PM_2.5 mass for 2000–2005 are compared against independent measurements not utilized for the adjustment approach. This comparison reveals that the adjusted model values have a lower root mean square error (RMSE) and higher correlation coefficients than the original model values. Furthermore, the PM_2.5 estimates from these adjusted model values are compared against an alternate method for estimating historic PM_2.5 values that is based on PM_2.5/PM₁₀ ratios calculated at co-located monitors. Results reveal that both methods yield estimates of historic PM_2.5 mass that are broadly consistent; however, the adjusted CMAQ values provide greater spatial coverage and information for PM_2.5 species in addition to total PM_2.5 mass. Finally, strengths and limitations of the proposed approach are discussed in the context of potential uses of this method.     

Indoor particulate matter in urban residences of Alexandria,Egypt

Indoor particulate matter samples were collected in 17 homes in an urban area in Alexandria during the summer season. During air measurement in all selected homes, parallel outdoor air samples were taken in the balconies of the domestic residences. It was found that the mean indoor PM_2.5 and PM₁₀ (particulate matter with an aerodynamic diameter ≤2.5 and ≤10 μm, respectively) concentrations were 53.5 ± 15.2 and 77.2 ± 15.1 µg/m³, respectively. The corresponding mean outdoor levels were 66.2 ± 16.5 and 123.8 ± 32.1 µg/m³, respectively. PM_2.5 concentrations accounted, on average, for 68.8 ± 12.8% of the total PM₁₀ concentrations indoors, whereas PM_2.5 contributed to 53.7 ± 4.9% of the total outdoor PM₁₀ concentrations. The median indoor/outdoor mass concentration (I/O) ratios were 0.81 (range: 0.43–1.45) and 0.65 (range: 0.4–1.07) for PM_2.5 and PM₁₀, respectively. Only four homes were found with I/O ratios above 1, indicating significant contribution from indoor sources. Poor correlation was seen between the indoor PM₁₀ and PM_2.5 levels and the corresponding outdoor concentrations. PM₁₀ levels were significantly correlated with PM_2.5 loadings indoors and outdoors and this might be related to PM₁₀ and PM_2.5 originating from similar particulate matter emission sources. Smoking, cooking using gas stoves, and cleaning were the major indoor sources contributed to elevated indoor levels of PM₁₀ and PM_2.5.

Implications: The current study presents results of the first PM_2.5 and PM₁₀ study in homes located in the city of Alexandria, Egypt. Scarce data are available on indoor air quality in Egypt. Poor correlation was seen between the indoor and outdoor particulate matter concentrations. Indoor sources such as smoking, cooking, and cleaning were found to be the major contributors to elevated indoor levels of PM₁₀ and PM_2.5.     

Objectives and Design of Central California's 1995 Integrated Monitoring Study of the California Regional PM10/PM2.5 Air Quality Study

ABSTRACT

The 1995 Integrated Monitoring Study (IMS95) is part of the Phase 1 planning efforts for the California Regional PM_10/PM_2.5 Air Quality Study. Thus, the overall objectives of IMS95 are to (1) fill information gaps needed for planning an effective field program later this decade; (2) develop an improved conceptual model for pollution buildup (PM10, PM2.5, and aerosol precursors) in the San Joaquin Valley; (3) develop a uniform air quality, meteorological, and emissions database that can be used to perform initial evaluations of aerosol and fog air quality models; and (4) provide early products that can be used to help with the development of State Implementation Plans for PM_10. Consideration of the new particulate matter standards were also included in the planning and design of IMS95, although they were proposed standards when IMS95 was in the planning process.     

Measurements and modelling of PM2.5 concentrations near a major road in Kuopio,Finland

A particle measurement campaign was conducted in a suburban environment near a major road in Kuopio, Central Finland from 3 August to 9 September 1999. The mass concentrations of fine particles (PM_2.5) were measured simultaneously at distances of 12, 25, 52 and 87 m from the centre of a major road at a height of 1.8 m, using identical samplers. The concentration measurements were conducted during 16 daytime hours (from 6.00 a.m. to 10.00 p.m.) for 27 days. Traffic flows and relevant meteorological parameters were measured on-site; meteorological measurements from a nearby synoptic weather station were also utilised. We also suggest a preliminary model for predicting the concentrations of PM_2.5 and apply this model in order to analyse the measured data. The regionally and long-range transported contribution was evaluated on the basis of a semi-empirical mathematical model utilising as input values the daily sulphate, nitrate and ammonium measurements at the EMEP stations (Co-operative programme for monitoring and evaluation of the long-range transmission of air pollutants in Europe). The influence of primary vehicular emissions from the nearest roads was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. The contribution of non-exhaust particulate matter emissions (including resuspension of particulate matter from road surfaces) was estimated simply to be directly proportional to the concentrations originating from primary vehicular emissions. Comparison of the predicted results and measurements yields information on the relative importance of various source categories of the measured concentrations of PM_2.5. The regionally and long-range transported contribution, the primary and non-exhaust vehicular emissions, and other sources were estimated to contribute on average 41±6%, 33±6% and 26±7% of the observed PM_2.5 concentrations, respectively. The model presented could also be applied in other European cities for analysing the source contributions to measured fine particulate matter concentrations.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Characterization of Particulate Matter in California

ABSTRACT

The size, composition, and concentration of particulate matter (PM) vary with location and time. Several monitoring/sampling programs are operated in California to characterize PM less than 2.5 and 10 µm in aerodynamic diameter (PM_2.5 and PM₁₀). This paper presents a broad summary of the spatial and temporal variations observed in ambient PM_2.5 and PM₁₀ concentrations in California. Many areas that have high PM₁₀ concentrations also have relatively high PM_2.5 concentrations, and data indicate that a significant portion of the PM₁₀ air quality problem is caused by PM_2.5. To develop effective plans for attaining the ambient PM standards, improved understanding of these unique problems is needed. Since 1989, pollution control efforts—whether specifically targeted for particulate matter or indirectly via controls on gaseous emissions—have caused annual average PM_2.5 and PM₁₀ concentrations to decline at most sites in California.     

Chemical characteristics of long-range transport aerosol at background sites in Korea

In this study, background concentration sites of Deokjeok and Gosan, which were deemed suitable for monitoring the impact of long-range transported air pollutants, were selected. An investigation of the source types of pollutants, their locations, and relative quantitative contributions to the particulate concentrations at both sites using appropriate methodologies to make initial estimations was conducted. Episodic measurements of PM_2.5, PM₁₀, and size distribution, along with its ion and carbon components were performed from 2005 to 2007, and a comprehensive analysis of the results was conducted utilizing back trajectory analysis. As for frequency of wind direction, it was quite apparent that the two sites are heavily influenced by air masses originating from the eastern and northern regions of China. For PM_2.5 and PM₁₀, the mass concentrations from north and east China were higher than other cases, originating from the ocean. In the northerly-wind case, meteorological properties for Deokjeok and Gosan and the influence of carbon emissions from northwest Korea resulted in a changing of air mass properties during transport. As was the case with mass concentration, the highest contribution for ionic and carbon components of PM_2.5 and PM₁₀ for both sites appeared for the westerly wind case. A specially high relative contribution, greater than 1.4 times, was apparent in the secondary aerosol case because of a large influence of long-range transported pollutants from east China. Carbon components exhibited different behaviors for the northerly and westerly wind cases compared with secondary aerosol. The major reason for this discrepancy appears to be the carbon emissions from northwest Korea.     

Resolving the interactions between population density and air pollution emissions controls in the San Joaquin Valley,USA

The effectiveness of emissions control programs designed to reduce concentrations of airborne particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) in California's San Joaquin Valley was studied in the year 2030 under three growth scenarios: low, medium, and high population density. Base-case inventories for each choice of population density were created using a coupled emissions modeling system that simultaneously considered interactions between land use and transportation, area source, and point source emissions. The ambient PM_2.5 response to each combination of population density and emissions control was evaluated using a regional chemical transport model over a 3-week winter stagnation episode. Comparisons between scenarios were based on regional average and population-weighted PM_2.5 concentrations. In the absence of any emissions control program, population-weighted concentrations of PM_2.5 in the future San Joaquin Valley are lowest under growth scenarios that emphasize low population density. A complete ban on wood burning and a 90% reduction in emissions from food cooking operations and diesel engines must occur before medium- to high-density growth scenarios result in lower population-weighted concentrations of PM_2.5. These trends partly reflect the fact that existing downtown urban cores that naturally act as anchor points for new high-density growth in the San Joaquin Valley are located close to major transportation corridors for goods movement. Adding growth buffers around transportation corridors had little impact in the current analysis, since the 8-km resolution of the chemical transport model already provided an artificial buffer around major emissions sources.

Assuming that future emissions controls will greatly reduce or eliminate emissions from residential wood burning, food cooking, and diesel engines, the 2030 growth scenario using “as-planned” (medium) population density achieves the lowest population-weighted average PM_2.5 concentration in the future San Joaquin Valley during a severe winter stagnation event.

Implications: The San Joaquin Valley is one of the most heavily polluted air basins in the United States that are projected to experience strong population growth in the coming decades. The best plan to improve air quality in the region combines medium- or high-density population growth with rigorous emissions controls. In the absences of controls, high-density growth leads to increased population exposure to PM_2.5 compared with low-density growth scenarios (urban sprawl).     

Emission of respirable dust from stone quarrying,potential health effects,and its management

Mining of minerals exerts adverse pressure on different compartments of environment directly or indirectly. Air is the worst affected environmental matrix, and it can carry the harmful effect of pollutants generated from mining activity even to distant places. The present study was undertaken to estimate the emission of particulate matter (PM_2.5 and PM₁₀) from different activities undertaken in stone quarrying in Mahendragarh, Haryana. The results obtained from the present study indicated that drilling, blasting, crushing, and transport of mined material are chiefly responsible for the generation of dust. Whereas drilling, blasting, and loading were responsible for emission of higher fraction of PM₁₀, crushing and re-suspension of roadside dust from movement of vehicles resulted in generation of relatively higher fraction of finer dust (PM_2.5). Modelling the transport of dust over the Hybrid Single-Particle Lagrangian Integrated Trajectory model revealed that the emitted particle may move up to the distance of about 40 km within 4 h of emission under average meteorological conditions. Fourier transform infrared (FTIR) spectroscopy analysis of dust confirmed the presence of calcite and gypsum, thus confirming the source as mining. The study concluded that generation of PM_2.5-sized particles may impose serious respiratory health effects over the workers engaged in mining, crushing, and transportation of sandstone. Apart from it, population residing downwind of the mining area is particularly vulnerable to the pulmonary effects due to inhalation of dust.

     

Chemical characteristics of fine particulate matters measured during severe winter haze events in Ulaanbaatar,Mongolia

In order to investigate the chemical characteristics of atmospheric aerosol measured during a severe winter haze event, 12-hr PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) samples were collected at an urban site in Ulaanbaatar, Mongolia, from January 9 to February 17, 2008. On average, 12-hr PM_2.5 mass concentration was 105.1 ± 34.9 μg/m³. Low PM_2.5 mass concentrations were measured when low pressure developed over central Mongolia. The 12-hr average organic mass by carbon (OMC) varied from 6.4 to 132.3 μg/m³, with a mean of 54.9 ± 25.4 μg/m³, whereas elemental carbon (EC) concentration ranged from 0.1 to 3.6 μgC/m³, with a mean of 1.5 ± 0.8 μgC/m³. Ammonium sulfate was found to be the most abundant water-soluble ionic component in Ulaanbaatar during the sampling period, with an average concentration of 11.3 ± 5.0 μg/m³. In order to characterize the effect of air mass pathway on fine particulate matter characteristics, 5-day back-trajectory analysis was conducted, using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The haze level was classified into three categories, based on the 5-day air mass back trajectories, as Stagnant (ST), Continental (CT), and Low Pressure (LP) cases. PM_2.5 mass concentration during the Stagnant condition was approximately 2.5 times higher than that during the Low Pressure condition, mainly due to increased pollutant concentration of OMC and secondary ammonium sulfate.

Implications: Mongolia is experiencing rapid rates of urbanization similar to other Asian countries, resulting in air pollution problems by the growing number of automobiles and industrialization. Ulaanbaatar, capital of Mongolia, is inherently vulnerable to air pollution because of its emission sources, topography, and meteorological characteristics. Very limited measurements on chemical characteristics of particulate matter have been carried out in Ulaanbaatar, Mongolia.     

An enhanced PM2.5 air quality forecast model based on nonlinear regression and back-trajectory concentrations

An enhanced PM_2.5 air quality forecast model based on nonlinear regression (NLR) and back-trajectory concentrations has been developed for use in the Louisville, Kentucky metropolitan area. The PM_2.5 air quality forecast model is designed for use in the warm season, from May through September, when PM_2.5 air quality is more likely to be critical for human health. The enhanced PM_2.5 model consists of a basic NLR model, developed for use with an automated air quality forecast system, and an additional parameter based on upwind PM_2.5 concentration, called PM24. The PM24 parameter is designed to be determined manually, by synthesizing backward air trajectory and regional air quality information to compute 24-h back-trajectory concentrations. The PM24 parameter may be used by air quality forecasters to adjust the forecast provided by the automated forecast system. In this study of the 2007 and 2008 forecast seasons, the enhanced model performed well using forecasted meteorological data and PM24 as input. The enhanced PM_2.5 model was compared with three alternative models, including the basic NLR model, the basic NLR model with a persistence parameter added, and the NLR model with persistence and PM24. The two models that included PM24 were of comparable accuracy. The two models incorporating back-trajectory concentrations had lower mean absolute errors and higher rates of detecting unhealthy PM2.5 concentrations compared to the other models.     

Identification of weather patterns impacting 24-h average fine particulate matter pollution

Methods are presented to extract intra-seasonal meteorological patterns at three scales to explain 24-h fine particulate matter (PM_2.5) pollution events: evolving large-scale meteorological scenarios, synoptic regimes driving diurnal variability near the surface, and localized meteorological triggers. The methods were applied to understand how winter weather conditions impacted PM_2.5 around the San Francisco Bay Area (SFBA). Analyzing data across 12 winters (November–March) ensured robust characterization of the SFBA conditions. SFBA 24-h PM_2.5 exceedances (35 μg m^?3) required several simultaneous characteristics: a ridge of aloft high pressure moving over SFBA, providing weak surface pressure gradients over Central California; persistent easterly flows through SFBA extending vertically to around the 925-hPa pressure level; orographically channeled winds resulting from stability; enhanced nocturnal cooling under clear-sky conditions providing for enhanced drainage flows off the Central California slopes; and at least two consecutive days of these conditions.     

Characteristics and sources of PM2.5 and carbonaceous species during winter in Taiyuan,China

Continuous observation of PM_2.5 was conducted in Taiyuan, a heavily polluted city in China, during high pollution season from December 2005 to February 2006. The results of this study showed that PM_2.5 and carbonaceous species pollution were serious during winter in Taiyuan. The organic carbon (OC) and element carbon (EC) were accounted for 18.6±11.2% and 2.9±1.6% of PM_2.5, respectively, which indicated that carbonaceous aerosols were key components for control fine particles pollution in Taiyuan. Coal combustion was a dominant source of OC and EC of PM_2.5 in the urban area of Taiyuan during winter. The impact of local and remote particle sources on urban air quality was assessed using PM_2.5 concentration rose and 3-day back trajectories of air masses arriving at Taiyuan. The meteorological conditions were found to affect the ambient concentrations of PM_2.5, OC, EC and OC/EC ratio.     

Particulate Air Pollution in Mexico City: A Collaborative Research Project

ABSTRACT

PM₁₀, PM₂₅, precursor gas, and upper-air meteorological measurements were taken in Mexico City, Mexico, from February 23 to March 22, 1997, to understand concentrations and chemical compositions of the city's particulate matter (PM). Average 24-hr PM₁₀ concentrations over the period of study at the core sites in the city were 75 H g/m³. The 24-hr standard of 150 μ g/m³ was exceeded for seven samples taken during the study period; the maximum 24-hr concentration measured was 542 μ g/m³. Nearly half of the PM₁₀ was composed of fugitive dust from roadways, construction, and bare land. About 50% of the PM₁₀ consisted of PM_2.5, with higher percentages during the morning hours. Organic and black carbon constituted up to half of the PM_2.5. PM concentrations were highest during the early morning and after sunset, when the mixed layers were shallow. Meteorological measurements taken during the field campaign show that on most days air was transported out of the Mexico City basin during the afternoon with little day-to-day carryover.     

Street-level concentrations of nitrogen dioxide and suspended particulate matter in Hong Kong

The concentrations of respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5) and nitrogen dioxide (NO₂) were measured in various locations over the territory of Hong Kong. In order to study the contributions of these pollutants from motor vehicles and their characteristics, the attention was focused on the roadside, street-level concentrations. A statistical analysis of the sampling results was conducted to obtain general characteristics of the roadside particulate and nitrogen dioxide pollution and to investigate the effects of traffic volume and meteorological factors on the pollution levels. High correlation coefficients are found between PM₁₀, PM_2.5 and NO₂ concentration.     

Effects of Wind Direction on Coarse and Fine Particulate Matter Concentrations in Southeast Kansas

Abstract

Field data for coarse particulate matter ([PM] PM₁₀) and fine particulate matter (PM_2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM₁₀ but not for PM_2.5. PM₁₀ concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM₁₀ or PM_2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM₁₀ and PM_2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from “other” directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.