Ambient Air Particulate Concentrations Research Articles

Measurements and predictions of ambient air particulates dry depositions at Taichung Shuinan Economic and Trade Park (T.S.E.T.P) during summer and autumn seasons

This study collected ambient air particulate concentrations and dry depositions at Taichung Shuinan Economic and Trade Park (T.S.E.T.P.) sampling site. The global dry depositions model was applied with known particle sizes, pollutant concentrations in ambient air pollutants dry deposition estimates at T.S.E.T.P. site in Central Taiwan. Statistical analysis of the correlated relationships among ambient air particulate concentration, dry depositions, and dry deposition velocities with respect to the meteorological conditions at T.S.E.T.P. site was also conducted. The results indicated that the average dry deposition fluxes were 377.81 µg/m2*min, the average TSP concentrations were 41.53 µg/m3, and the average dry depositions velocity (Vd ) was 15.26 cm/s. In addition, 40 μm was selected as the best ambient air particulate size for the calculated dry deposition velocities to estimate ambient air dry deposition fluxes using the Global model at this T.S.E.T.P. sampling site. Moreover, ambient air particulates concentrations were higher in the fall season than that of the summer season. Sincepollutants parcels were not easily diffused during the fall season because of the low mixed atmospheric layer in this season. Therefore, atmospheric particulates concentrations were higher from the northern side than that of southern side in this study. Furthermore, wind speed V.S. particulate dry depositions and wind V.S. dry depositions velocity displayed a moderate correlated relationship, while particulate dry depositions V.S. particulate dry depositions velocity presented the highest correlated relationship at this T.S.E.T.P. sampling site. However, particulate dry depositions and dry depositions velocity presented significant differences. Finally, particulate dry depositions and dry depositions velocity were not only highly correlation related but also displayed significant differences at the T.S.E.T.P. sampling site.

Monitoring ambient air particulates, VOC and CO2 pollutants concentrations, particulates numbers by AQ Guard Ambient sampler

The AQ Guard Ambient sampler is the most advanced compact device that collects ambient air pollutants of various particle sizes. This study collected ambient air pollutant particles of various sizes, CO2 and VOC concentrations and particle numbers from April to May of 2021 using the AQ-guard ambient sampler at Taichung Science Park. The relationships among various particles sizes, CO2, VOC concentrations and total particle numbers in conjunction with the meteorological conditions are also discussed. Appropriate statistical methods were used to test the mean concentration differences between various particulate sizes, CO2, VOC and total particle numbers at Taichung Science Park. The results indicated that particle concentrations of various sizes (PM1, PM2.5, PM4, PM10, PMtot), CO2, VOC concentrations and particulate numbers at the sample location over the study period were the highest on April 21 and gradually decreased from April 21 to May 23, 2021 at Taichung Science Park. The meteorological conditions; temperature, relative humidity and wind speed were negatively displayed but moderately correlated with the ambient air particulate concentrations, CO2, VOC and particulate numbers. The relationships between ambient air particles (PM1, PM2.5, PM4, PM10, PMtot), and CO2, VOC concentrations displayed moderate correlated relationships. The results further showed that there were mean concentration differences existing for most of the pollutants (particulates, CO2 and VOC) monitored by the AQ Guard Ambient sampler in this study. The relationships among particles sizes of PM1 v.s. PM2.5, PM2.5 v.s. PM4, PM4 v.s. PM10, PM10 v.s. PMtot were highly correlated with the mean concentrations. However, the results further showed that they also displayed no significant mean concentration differences for PM1 v.s. PM2.5, PM2.5 v.s. PM4, PM4 v.s. PM10, PM10 v.s. PMtot were collected by the AQ Guard sampler at this Taichung Science Park sampling site.

Particle size distributions and seasonal concentrations study of atmospheric pollutants (particulates, Hg(p)) at an agricultural site in Taiwan

The concentrations of ambient air particulates of various sizes (PM1, PM2.5, PM6.25, PM10 and PM18) were measured using a MOUDI sampler at an agricultural sampling site in central Taiwan from January to September, 2020. The concentrations of particulate-bound mercury Hg(p) that were attached to particulates of various sizes (PM1, PM2.5, PM6.25, PM10 and PM18)) were analyzed using a Direct Mercury Analyzer (DMA-80). The results revealed that the mean particulate and Hg(p) concentrations associated with PM18, PM10, PM6.25, PM2.5 and the back filter were all highest in autumn and winter. The mean mass median diameter (m.m.d.) value for ambient air particulates was highest in the summer and lowest for Hg(p). The mean m.m.d. value for ambient air Hg(p) was highest in the winter and lowest for ambient air particulates. The northeast monsoon prevails in the winter season of Taiwan. Thus, The sources of particulates and Hg(p) pollutants might came from the northeast side of Taiwan - especially in spring and winter seasons. Thus, ambient air particulate concentrations are higher in the winter than in the spring. However, ambient air particulate-bound mercury Hg(p) tended to be associated more with coarse particulates than with other particulates. Finally, Hg(p) concentrations in Taiwan have increased over the last 15 years. Reducing atmospheric Hg(p) concentrations has become an important environmental goal in all countries - especially those in Asia.

Particulates and metallic elements pollutants concentrations, dry depositions and sources study at a suburban area

In this study, the concentrations and dry deposition of ambient air particulates are measured using a PS-1 sampler and a dry deposition plate at a suburban sampling site in central Taiwan from January 2019 to August 2019. The concentrations of metallic elements that are attached to the particulates and dry deposits are analyzed using inductively coupled plasma atomic emission spectroscopy (ICP-AES). The source analysis model was used to identify possible emission sources of the pollutants and the transmission distributions of ambient air metallic elements. During the sampling period, the TSP concentration at the Peiying suburban sampling site was highest on January 24th (in winter) and lowest on April 29th (in spring). Dry deposition was greatest on January 9th (in winter) and least on July 29th (in summer). In addition, the correlation coefficient between temperature and ambient air particulate concentration revealed a moderate to strong negative relationship (r2 = 0.60) the ambient air particulate concentrations declined throughout the study period. Moreover, high concentrations of crustal metallic elements Al, Ca were found in this study due to the particulates suspensions from the roads in the suburban area. The Taiwan II express highway runs about 500 m to the northeast of the sampling site, explaining why high concentrations of Cu were measured in this study. The concentrations of metallic elements Ca, Cd, Ni, Pb, and Zn were highest in winter and spring while those of metallic elements Al, Cu, Cr, and Mn were highest in summer. The mean metallic elemental concentrations followed the order: Ca > Al > Zn > Cu > Mn > Ni > Pb > Cr > Cd. The dry depositions of metallic elements Ca, Pb, Zn, Ni and Cd were greatest in winter and spring and those of metallic elements Al, Cu, Cr and Mn were highest in summer. The average dry depositions of the studied metallic elements followed the order: Ca > Al > Zn > Cu > Mn > Ni > Pb > Cr > Cd. The main pollution parcel was from the north side of Taiwan, pollution parcel that associated with the northeast monsoon, resulted in ambient air pollutants emitted on Taiwan island, influencing its air quality.

Assessing the impact of fine particulate matter (PM2.5) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates

An enhanced research paradigm is presented to address the spatial and temporal gaps in fine particulate matter (PM2.5) measurements and generate realistic and representative concentration fields for use in epidemiological studies of human exposure to ambient air particulate concentrations. The general approach for research designed to analyze health impacts of exposure to PM2.5 is to use concentration data from the nearest ground-based air quality monitor(s), which typically have missing data on the temporal and spatial scales due to filter sampling schedules and monitor placement, respectively. To circumvent these data gaps, this research project uses a Hierarchical Bayesian Model (HBM) to generate estimates of PM2.5 in areas with and without air quality monitors by combining PM2.5 concentrations measured by monitors, PM2.5 concentration estimates derived from satellite aerosol optical depth (AOD) data, and Community-Multiscale Air Quality (CMAQ) model predictions of PM2.5 concentrations. This methodology represents a substantial step forward in the approach for developing representative PM2.5 concentration datasets to correlate with inpatient hospitalizations and emergency room visits data for asthma and inpatient hospitalizations for myocardial infarction (MI) and heart failure (HF) using case-crossover analysis. There were two key objective of this current study. First was to show that the inputs to the HBM could be expanded to include AOD data in addition to data from PM2.5 monitors and predictions from CMAQ. The second objective was to determine if inclusion of AOD surfaces in HBM model algorithms results in PM2.5 air pollutant concentration surfaces which more accurately predict hospital admittance and emergency room visits for MI, asthma, and HF. This study focuses on the New York City, NY metropolitan and surrounding areas during the 2004–2006 time period, in order to compare the health outcome impacts with those from previous studies and focus on any benefits derived from the changes in the HBM model surfaces. Consistent with previous studies, the results show high PM2.5 exposure is associated with increased risk of asthma, myocardial infarction and heart failure. The estimates derived from concentration surfaces that incorporate AOD had a similar model fit and estimate of risk as compared to those derived from combining monitor and CMAQ data alone. Thus, this study demonstrates that estimates of PM2.5 concentrations from satellite data can be used to supplement PM2.5 monitor data in the estimates of risk associated with three common health outcomes. Results from this study were inconclusive regarding the potential benefits derived from adding AOD data to the HBM, as the addition of the satellite data did not significantly increase model performance. However, this study was limited to one metropolitan area over a short two-year time period. The use of next-generation, high temporal and spatial resolution satellite AOD data from geostationary and polar-orbiting satellites is expected to improve predictions in epidemiological studies in areas with fewer pollutant monitors or over wider geographic areas.

Ambient air metallic pollutant study at HAF areas during 2013–2014

This study characterized diurnal variations of the total suspended particulate (TSP) concentrations, dry deposition flux and dry deposition velocity of metallic elements at Taichung Harbor (Harbor), Gong Ming Junior High School (Airport) and Sha lu Farmland (Farmland) sampling sites in central Taiwan between August, 2013 and July, 2014 in this study. The result indicated that: 1) the ambient air particulate concentrations, dry depositions were displayed as Harbor>Farmland>Airport during the day time sampling period. However, dry deposition velocities were shown as Airport>Harbor>Farmland for this study. 2) The ambient air particulate concentrations, dry depositions were displayed as Airport>Harbor>Farmland during the night time sampling period. However, dry deposition velocities were shown as Farmland>Harbor>Airport for this study. 3) The metallic element Zn has the average highest concentrations at Airport, Harbor and Farmland among all the metallic elements during the day time sampling period in this study. 4) There were significant differences for the metallic elements (Cr, Cu, Zn and Pb) in dry depositions at these three characteristic sampling sites (HAF) for the night time sampling period. The only exception is metallic element Cd. It displayed that there were no significant differences for the metallic element Cd at the Airport and Farmland sampling sites during the night time sampling period. 5) The average highest values for the metallic element Cu in TSP among the three characteristic sampling sites occurred during the fall and winter seasons for this study. As for the dry depositions, the average highest values in dry deposition among the three characteristic sampling sites occurred during the spring and summer seasons for this study. 6) The average highest values for the metallic element Cd in TSP among the three characteristic sampling sites occurred during the spring and summer seasons for this study. As for the dry depositions, the average highest values in dry deposition among the three characteristic sampling sites occurred during fall and winter for this study.

Diurnal concentrations variations, size distributions for ambient air particles and metallic pollutants (Cr, Mn, Ni, Cd, Pb) during summer season at a traffic area

This study characterized and discussed particulate ambient air particulate concentrations and seasonal variations for PM18, PM10, PM2.5, and PM1 during June 2013-July 2013 at this traffic sampling site. In addition, this study also characterized the ambient air particulates size distributions by using MOUDI-100S4 sampler to collect 1-day the ambient suspended particles (PM18, PM10, PM2.5, and PM1) at this sampling site. In addition, the study also showed that the main pollutants contributions were from traffic and residual areas. As for the pollutants seasonal concentrations variations, the results indicated that the average particle concentrations orders were all displayed as daytime > nighttime for PM18, PM10, PM2.5 and PM1 at this characteristic sampling site. The results further indicated that the mean highest of metal concentrations in this study indicated that the average metal concentration were all displayed as Mn > Cr > Ni > Pb > Cd for PM18, PM10, PM2.5 and PM1 on daytime and nighttime at this characteristic sampling site.

Measuring metallic elements of total suspended particulates (TSPs), dry deposition flux, and dry deposition velocity for seasonal variation in central Taiwan

The purposes of this study were to investigate and characterize ambient air particulate concentrations and dry deposition fluxes and those metallic elements associated with them in central Taiwan during 2009–2010. At the Bei-Shi (suburban/coastal) sampling site, the average TSP concentrations, flux, and deposition velocity were 92.3 (μgm−3), 165.92 (μgm−2min−1), and 3 (cms−1), respectively. In addition, at the Quan-Xin (industrial) sampling site, the highest average TSP concentrations of As, ambient air particulate bound mercury (Hg(p)), Mn, Fe, Zn, Cr, Cu and Pb were 3.56, 0.07, 82.21, 2769.82, 109.33, 31.93, 109.76 and 39.15ngm−3, respectively. The highest average dry deposition flux of elements As, Hg(p), Mn, Fe, Zn, Cr, Cu and Pb were 2.36, 0.15, 18.11, 303.8, 35.59, 18.66, 45.47 and 42.6 (ngm−2min−1), respectively in Quan-Xin (industrial). The median deposition velocity (Vd) of metallic elements was present at the five characteristic sampling sites, respectively. However, the median dry deposition velocity (Vd) of Hg(p) was 2.73–4.78cms−1, higher than all other elements in the five characteristic sampling sites during 2009–2010. The median dry deposition velocity (Vd) of Fe was 0.17–0.27cms−1, lower than all other elements in the five characteristic sampling sites during 2009–2010.

Study of Atmospheric Particulates and Metallic Pollutants in Harbor and Traffic Areas in Central Taiwan

This study measured ambient air particulate concentrations by PS-1 and USA samplers at Taichung Harbor (TH) and WuChi traffic (WT) near the Taiwan Strait in central Taiwan during four seasons (Marc...

Particulate and Ionic Species Near Highway Traffic During Day and Night

Ambient air particulate concentrations were sampled using an RP2300 Sampler (RP 2300, Rupprecht and Patashnick, CO, USA) from March 4 to April 2, 2006, at a sampling site near highway traffic. However particles that have an aerodynamic diameter of less than 10 μm (0.01 mm) are called respiratory particulates or particulate matter (PM10). PM2.5 denotes fine particles with an aerodynamic diameter of less than 2.5 μm (Ohlstrom et al., 2000). Analysis results indicated that the average mass concentrations of fine particles (PM2.5) and inhalable particles (PM10) were 64.43 and 109.20 μ g/m3, respectively, during the day. The average mass concentrations of PM2.5 and PM10 were 69.45 and 110.46 μ g/m3 during the night. Average concentrations of major ionic species, NO2 −, NO3 −, SO4 2 −, and NH4 + during the day were 2.37, 9.16, 13.72, and 7.70 μ g/m3 in PM2.5 and 3.72, 16.42, 16.90, and 8.91 μ g/m3 in PM10. The corresponding values at night were 3.29, 11.77, 11.55, and 8.03 μ g/m3 in PM2.5 and 3.87, 18.13, 14.27, and 9.98 μ g/m3 in PM10. However, t-test results revealed that the concentrations of ionic species (Cl−, NO2 −, NH4 +, Mg2 +, and Ca2 +) in PM2.5 during the day did not differ significantly from those at night. T-test results revealed that the ionic species (NO3 −, SO4 2 −, Na+, and K+) concentrations in PM2.5 during the day differ, significantly from those at night. The concentrations of ionic species (Cl−, NO2 −, NO3 −, and NH4 +) in PM10 during the day did not differ significantly from those at night. The concentrations of ionic species (SO4 2 −, Na+, K+, Mg2 +, and Ca2 +) in PM10 during the day differed significantly from those at night. The concentration of Cl− in PM2.5 is negatively correlated (r = −0.62) with the temperature during the day at this site close to highway traffic. Furthermore, a comparison with previous results (Fang et al., 1999a) revealed that the average concentration of NO3 − increased by 32% and 46% in PM2.5 and PM10, respectively, during the day. Average concentrations of NO3 − were increased by 72% and 65% in PM2.5 and PM10, respectively, during the night. Average concentrations of SO4 2 − were decreased by 10% and 7% in PM2.5 and PM10, respectively, during the day. Finally, average concentrations of SO4 2 − were decreased by 72% and 62% in PM2.5 and PM10, respectively, during the night. This monitoring data may be helpful to establish baseline or background concentrations, which is essential for forensic investigations.

Ambient air particulate concentrations and metallic elements principal component analysis at Taichung Harbor (TH) and WuChi Traffic (WT) near Taiwan Strait during 2004–2005

The purpose of this study is to characterize metallic elements associated with atmospheric particulate matter of total suspended particulate (TSP), fine particle (particle matter with aerodynamical diameter <2.5μm, PM2.5), coarse particle (particle matter with aerodynamical diameter 2.5–10μm, PM2.5–10) at the Taichung Harbor (TH) and WuChi Traffic (WT) sampling site of central Taiwan during March 2004 to February 2005. The result indicated the average total suspended particulate concentration in 1 year was 157.31 and 112.58μgm−3 at TH and WT sampling site, respectively. Fine particle (PM2.5) size was the dominant species at TH and WT sampling site. In TH sampling site, higher correlation coefficient was observed on total suspended particulates of metallic elements Fe and Zn. And in WT sampling site, higher correlation coefficients displayed on total suspended particulates of metallic elements Fe and Zn, Fe and Mn. Ambient airborne particle principal component analysis of metallic metals was used to identify the possible pollutant sources in this study. At the TH sampling site, 50.81% of the total variance of the data was observed in factor 1. Higher loading of Fe (0.86), Zn (0.79), Pb (0.76), and Mn (0.68) were contributed by traffic emission and the soil source. At the WT sampling site, factor 1 explained 53.74% of the total variance of the data and had high loading for Zn (0.86) and Cu (0.85), which were identified as industrial/traffic emission sources.

Fraction of PM2.5 Personal Exposure Attributable to Urban Traffic: A Modeling Approach

Personal exposure to fine particles (PM2.5) in a nonsmoking adult population has been characterized in Grenoble, France, in the framework of the European EXPOLIS study. The objective of this paper is to assess the fraction of the PM2.5 personal exposure attributable to urban traffic emissions. Volunteers (n = 40) carried a personal exposure monitoring case and filled in questionnaires on their outdoor and indoor environments, as well as time-activity diaries (15 min resolution), during 48 h (working days). Workplaces and places of residence were classified in two categories using a Geographic Information System (GIS): the atmospheric environment of some volunteers is best represented by PM ambient air monitors located in urban background sites, and others by monitors situated close to high traffic density sites (proximity sites). A partial least-squares regression model estimated the PM2.5 personal exposure (average = 36.6 μg/m3 ; standard deviation = 23.4 μg/m3) as a function of time spent in proximity (at work, home, or commuting), PMIO ambient air levels during the same days, and several confounders (passive smoking and indoor sources of particles). Six scenarios of “proximity” and “background” environments were accommodated, according to traffic intensity and road distance, in a sensitivity analysis; the best fitted model had R2 = .7. Personal PM2.5 exposures predicted by this model for different segments of the study population were compared to the background personal exposure, thus providing an estimate of the additional contribution of time spent near traffic sources. On average (percent time spent in proximity = 16.3; proximity scenario defined as the area located less than 50 m from a street with a traffic intensity greater than 20,000 vehicles/day), the PM2.5 personal exposure attributable to traffic equals 30%. For the lower tercile of the population, this contribution is 26%; for the upper tercile, it is 45%. A very influential parameter of this modeling estimation is the proportion of background ambient air particulate concentrations associated with traffic emissions. Based on local nighttime/daytime concentration ratios, a 20% proportion has been derived and used for these results. In the literature, this parameter ranges from 10% to 60%, yielding a proportion of personal exposure attributable to traffic proximity between 20% and 60%, and high-exposure situations reaching 60 to 75%. While these estimates are based only on winter data, they are in agreement with other results published in the literature. This modeling approach might be applied to other metropolitan situations, insofar as local data are used to assess the influence of traffic emissions on background ambient air PM 10 concentrations.