Highest Mean PM2 Research Articles

Seasonal variation in exposure to particulate matter among children attending different levels of education: Comparison of two dosimetry models

Exposure to particulate matter (PM) has been associated with several adverse health outcomes. Studies indicate that children may be exposed to much higher concentrations of PM at school than in other environments. There exists very little data on the deposited dose of PM while children attend classes. This study was carried out in a school located near an industrial complex in Portugal and attended by children aged 3–12 years. Indoor PM10, PM2.5 and PM1 were measured over two seasons in classrooms representing different school year groups. Particle deposition fractions in the respiratory tract, as well as the deposited doses, were calculated using the Multiple-Path Particle Dosimetry (MPPD) and the Exposure Dose Model (ExDoM2). Both models were implemented assuming an 8-h exposure scenario to represent the school day. In general, differences in PM concentrations were observed depending on room occupancy periods and season. The highest mean PM2.5 concentration was recorded in winter when the classroom was vacant (23.7 ± 20.5 μg m−3), while the highest mean PM10 level was observed in spring during school hours (61.7 ± 24.2 μg m−3). Regardless of the dosimetry model, the highest deposition of PM10 and PM2.5 was in the upper region, while the lowest was in the tracheobronchial (TB) region. The results indicate that deposited dose and deposition fraction in spring may be more harmful to pupils’ health than in winter. PM10 presented the highest doses, ranging from 54.2 to 128 μg and from 83.9 to 185 μg, according to MPPD and ExDoM2 estimates, respectively.

PM2.5-bound heavy metals in a typical industrial city of Changzhi in North China: Pollution sources and health risk assessment

Heavy metal contamination in fine particulate matter (PM2.5) emitted in industrial cities is extremely severe. Hence, determining their pollution sources apportionment is critical to inform effective pollution control strategies for reducing the risks associated with heavy metals in PM2.5. This study comprehensively investigated the characteristics, source apportionment, and health risks associated with heavy metals in PM2.5 at three monitoring stations—Jiance, Qinghua, and Shenjiju—in Changzhi, a typical industry city in China, during three seasons: autumn and winter of 2017 and summer of 2018. The daily average concentrations of PM2.5 (65.68 μg m−3) during the study period were lower than the second-grade limit value established in China (75 μg m−3). The highest mean PM2.5 concentrations were observed during winter (72.45 μg m−3) and at the Jiance station (64.51 μg m−3), respectively. At Qinghua station, vehicle emissions contributed more V to PM2.5, whereas Jiance station showed higher levels of Pb and Zn from coal combustion. In autumn, there was an increased in the Fe content in PM2.5 from vehicle emissions, whereas elevated concentrations of Cr and Ni from industry sources were observed in summer. According to the results of a positive matrix factorization, the main sources of PM2.5-bound heavy metals in Changzhi were coal combustion (34.3%), vehicle emissions (30.9%), fugitive dust (21.8%) and industry (13.0%). The major sources during autumn, winter, and summer were vehicle emissions (39.0%), coal combustion (35.0%), and industrial emissions (61.7%), respectively, showing a clear seasonal pattern. At both the Jiance station and Shenjiju station, extensive coal-based heating was the primary source of emissions, whereas vehicle emissions were the main source of emissions at QH owing to its high levels of traffic. The total hazard quotient (HQ) values for heavy metals were below the safe level (HQ = 1); but the carcinogenic risks (CR) exceed the lower limit of tolerance (1E-6). Stronger emissions from industrial sources in summer and the large amount of coal combustion for heating at the Jiance station are possible reasons for the higher health risk of heavy metals. Identification the source of PM2.5-bound heavy metals could support the targeted reduction of associated emissions, consequently helping alleviate heavy metals contamination and control associated risks to human health.

Partitioning personal measurements into personal exposure to pollution from indoor and outdoor sources in London inhabitants - Αn analysis within the MELONS* project

Background and Aim. Air pollution, particularly particles, from indoor and outdoor sources originate from different activities, have different toxicities, may have different health effects and need to be addressed by separate interventions. For epidemiological studies, personal exposure to pollution from outdoor sources was proposed as true exposure often approximated by modelled estimates. This study aimed to derive partitioned personal exposure to pollution from indoor and outdoor sources from four diverse study populations gathering 13,871 participant-day personal measurements of exposure using wearables in London. Methods. A mixed approach, based on personal measurement frequency, fixed radius, clustering and reverse geocoding, was applied to tag one-minute personal measurements into personal microenvironments. Home addresses were paired with local ambient data to facilitate calculation of infiltration efficiencies. Then personal measurements were partitioned into personal exposure to pollution from indoor and outdoor sources at one-minute resolution for each participant. Results. COPD patients, compared to school children, had higher mean PM2.5 pollution from indoor sources (COPD: 6.9 ± 37.2, children: 4.1 ± 42.8 μg/m3), but similar pollution from outdoor sources (COPD: 5.1 ± 12.8, children: 5.3 ± 27.2 μg/m3). For black carbon, professional drivers, compared to healthy adults, had higher personal exposure to pollution from outdoor sources (drivers: 1.7 ± 4.3, healthy adults: 1.2 ± 2.5 μg/m3) and lower personal exposure to pollution from indoor sources (drivers: 0.3 ± 1.8, healthy adults: 0.8 ± 2.1 μg/m3). Conclusions. By utilising GPS data, personal measurements from wearable monitors can be partitioned into personal exposure to pollution from indoor and outdoor sources to aid epidemiological studies and policy development. Our results, highlight that sources of pollution can be very different even though personal measurements of total exposure levels are similar. Further work is required to extrapolate results over longer time periods and larger populations. Keywords. Pollution partition, Personal exposure; Wearables

Spatiotemporal Variations of Particulate Matter and their Association with Criteria Pollutants and Meteorology in Malaysia

Fine particulate matter (PM2.5) poses a higher risk to human health than coarse particulate matter (PM10). This study aims to determine the spatiotemporal variations of PM2.5 and PM10 in Malaysia and their association with other criteria pollutants and meteorological factors. Hourly data from air quality monitoring stations for the year 2018 were retrieved from the Malaysian Department of Environment and analysed for temporal and spatial scales according to different regions in Malaysia. Further statistical analyses, such as Spearman’s Rank Correlation and Principal Component Analysis (PCA), were conducted to study the associations between PM2.5 and PM10 with other main criteria air pollutants, as well as meteorological parameters. Higher mean concentrations of PM2.5 (23 ± 8 µg/m3, range = 4.6–158 µg/m3) and PM10 (32 ± 10 µg/m3, range = 6.0–181 µg/m3) were observed in the central region of the Malaysian Peninsula. The diurnal patterns of PM2.5 and PM10 were in a bimodal pattern and influenced by traffic emissions. The highest mean PM2.5 and PM10 concentrations were recorded during the southwest monsoon season, notably in the central region. The Spearman’s Rank Correlation shows that NO2 and CO have a moderately positive correlation (p < 0.01) with PM2.5 (r = 0.47) and PM10 (r = 0.48) in the central regions while all meteorological parameters show significantly weak to very weak correlations with PM. The PCA analysis indicates that the major sources leading to the formation of particulate matter are from the contribution of secondary aerosols and combustion-related sources. The ratio of PM2.5 to PM10 ranged between 0.51 and 0.76 nationwide with the highest mean recorded in the central region (0.72). This study indicates that there is a higher abundance of fine particulate in the ambient air of the urbanised environment and thus a greater likely risk to human health in more developed areas.

Seasonal variations in carbonaceous species of PM2.5 aerosols at an urban location situated in Indo-Gangetic Plain and its relationship with transport pathways, including the potential sources

The study examines the variation in organic carbon (OC) and elemental carbon (EC) in PM2.5 concentration at an urban location of Indo-Gangetic Plains (IGP) to understand the impact of seasonality and regional crop residue burning activities. Seasonal cluster analysis of backward air masses and concentration-weighted trajectory (CWT) analysis was performed to identify seasonal transport pathways and potential source regions of carbonaceous aerosols. The mean PM2.5 level during the study period was 57 ± 41.6 μgm−3 (5.0–187.3 μgm−3), whereas OC and EC concentration ranges from 2.8 μgm−3 to 28.2 μgm−3 and 1.3 μgm−3 to 15.5 μgm−3 with a mean value of 8.4 ± 5.5 μgm−3 and 5.1 ± 3.3 μgm−3 respectively. The highest mean PM2.5 concentration was found during the winter season (111.3 ± 25.5 μgm−3), which rises 3.6 times compared to the monsoon season. OC and EC also follow a similar trend having the highest levels in winter. Total carbonaceous aerosols contribute ∼38% of PM2.5 composition. The positive linear trend between OC and EC identified the key sources. HYSPLIT cluster analysis of backward air mass trajectories revealed that during the post-monsoon, winters, pre-monsoon, and monsoon, 71%, 81%, 60%, and 43% of air masses originate within the 500 km radius of IGP. CWT analysis and abundance of OC in post-monsoon and winters season establish a linkage between regional solid-biomass fuel use and crop residue burning activities, including meteorology. Moreover, the low annual average OC/EC ratio (1.75) indicates the overall influence of vehicular emissions. The current dataset of carbonaceous aerosols collated with other Indian studies could be used to validate the global aerosol models on a regional scale and aid in evidence-based air pollution reduction strategies.

The association between air pollutants and hippocampal volume from magnetic resonance imaging: A systematic review and meta-analysis

Growing epidemiological evidence suggests that air pollution may increase the risk of cognitive decline and neurodegenerative disease. A hallmark of neurodegeneration and an important diagnostic biomarker is volume reduction of a key brain structure, the hippocampus. We aimed to investigate the possibility that outdoor air nitrogen dioxide (NO2) and particulate matter with diameter ≤2.5 μm (PM2.5) and ≤10 μm (PM10) adversely affect hippocampal volume, through a meta-analysis. We considered studies that assessed the relation between outdoor air pollution and hippocampal volume by structural magnetic resonance imaging in adults and children, searching in Pubmed and Scopus databases from inception through July 13, 2021. For inclusion, studies had to report the correlation coefficient along with its standard error or 95% confidence interval (CI) between air pollutant exposure and hippocampal volume, to use standard space for neuroimages, and to consider at least age, sex and intracranial volume as covariates or effect modifiers. We meta-analyzed the data with a random-effects model, considering separately adult and child populations. We retrieved four eligible studies in adults and two in children. In adults, the pooled summary β regression coefficients of the association of PM2.5, PM10 and NO2 with hippocampal volume showed respectively a stronger association (summary β −7.59, 95% CI −14.08 to −1.11), a weaker association (summary β −2.02, 95% CI −4.50 to 0.47), and no association (summary β −0.44, 95% CI −1.27 to 0.40). The two studies available for children, both carried out in preadolescents, did not show an association between PM2.5 and hippocampal volume. The inverse association between PM2.5 and hippocampal volume in adults appeared to be stronger at higher mean PM2.5 levels. Our results suggest that outdoor PM2.5 and less strongly PM10 could adversely affect hippocampal volume in adults, a phenomenon that may explain why air pollution has been related to memory loss, cognitive decline, and dementia.

Household Air Pollution (HAP) is Associated With Increased Pneumococcal Carriage in Malawian Infants – Malawi&amp;nbsp; &lt;i&gt;Streptococcus pneumoniae&lt;/i&gt;&amp;nbsp;Carriage and Air Pollution Exposure (MSCAPE) Study

Background: Household air pollution (HAP) from solid cooking fuels is a risk factor for childhood pneumonia. Nasopharyngeal carriage of Streptococcus pneumoniae is a necessary step in development of pneumococcal pneumonia. We hypothesised that HAP influences S. pneumoniae carriage. MSCAPE assesses the relationship between HAP from solid cooking fuels and prevalence/density of S. pneumoniae carriage among children. Methods: MSCAPE is a prospective study of children participating in a trial of a cleaner burning cookstove to reduce childhood pneumonia incidence in Malawi (CAPS). MSCAPE assessed the impact of HAP exposure on prevalence of S. pneumoniae carriage in 6 week and 6 month old children through intention-to-treat and adjusted exposure-response analyses. Exposure to HAP (PM2·5) was assessed through RTI Micropem and S. pneumoniae detection in nasopharyngeal swabs. Findings: S. pneumoniae carriage prevalence was 39% (n=89) and 85% (n=398) in 6 week and 6 month old children respectively. A higher mean PM2·5 exposure was observed in intervention children (53·9 µg/m3) than controls (49·0 µg/m3), although not significant (p>0.05). Intention-to-treat analysis found a non-significant increased risk of S. pneumoniae carriage in intervention children (OR=1·36; 95% CI:0·97, 1·92). Exposure-response analysis found a significant association between HAP exposure and S. pneumoniae carriage with a 10% increase in risk per decile of PM2·5 exposure (OR=1·10; 95% CI:1·01, 1·20, p=0·035). Interpretation: Despite the lack of observed impact from the cleaner burning cookstove intervention, HAP exposure (PM 2·5) was significantly associated with S. pneumoniae prevalence giving empirical evidence for the potential mechanistic relationship between HAP and childhood pneumonia. Funding Statement: This work was supported by Bill and Melinda Gates Foundation through grant number OPP1131425. The CAPS was funded by a Joint Global Health Trials Grant from the Medical Research Council, the UK Department for International Development and Wellcome Trust (MR/K006533/1). Declaration of Interests: There are no financial competing interests in relation to the work described. Ethics Approval Statement: Ethical approval was provided by the College of Medicine Research and Ethics Committee (COMREC), Malawi (P.08/15/1794) and the Central Ethics Committee of the University of Liverpool (RETH000839).

Concurrent assessment of personal, indoor, and outdoor PM2.5 and PM1 levels and source contributions using novel low-cost sensing devices.

The intensity, frequency, duration, and contribution of distinct PM2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM2.5 and PM1 monitoring using novel low‐cost sensing (LCS) devices, AS‐LUNG. GRIMM‐comparable observations were acquired by the corrected AS‐LUNG readings, with R 2 up to 0.998. Twenty‐six non‐smoking healthy adults were recruited in Taiwan in 2018 for 7‐day personal, home indoor, and home outdoor PM monitoring. The results showed 5‐min PM2.5 and PM1 exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m3, respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM2.5 increments of 76.5 and 183.8 µg/m3 (1 min), respectively. Incense burning had the highest mean PM2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5‐min PM2.5 increments (15.0 µg/m3) to indoor levels, among all single sources. Certain events accounted for 14.0%‐39.6% of subjects’ daily exposures. With the high resolution of AS‐LUNG data and detailed time‐activity diaries, the impacts of sources and ventilations were assessed in detail.

Demonstration of a Low-Cost Multi-Pollutant Network to Quantify Intra-Urban Spatial Variations in Air Pollutant Source Impacts and to Evaluate Environmental Justice.

Air quality monitoring has traditionally been conducted using sparsely distributed, expensive reference monitors. To understand variations in PM2.5 on a finely resolved spatiotemporal scale a dense network of over 40 low-cost monitors was deployed throughout and around Pittsburgh, Pennsylvania, USA. Monitor locations covered a wide range of site types with varying traffic and restaurant density, varying influences from local sources, and varying socioeconomic (environmental justice, EJ) characteristics. Variability between and within site groupings was observed. Concentrations were higher near the source-influenced sites than the Urban or Suburban Residential sites. Gaseous pollutants (NO2 and SO2) were used to differentiate between traffic (higher NO2 concentrations) and industrial (higher SO2 concentrations) sources of PM2.5. Statistical analysis proved these differences to be significant (coefficient of divergence > 0.2). The highest mean PM2.5 concentrations were measured downwind (east) of the two industrial facilities while background level PM2.5 concentrations were measured at similar distances upwind (west) of the point sources. Socioeconomic factors, including the fraction of non-white population and fraction of population living under the poverty line, were not correlated with increases in PM2.5 or NO2 concentration. The analysis conducted here highlights differences in PM2.5 concentration within site groupings that have similar land use thus demonstrating the utility of a dense sensor network. Our network captures temporospatial pollutant patterns that sparse regulatory networks cannot.

The Association of Long-Term Exposure to Particulate Matter Air Pollution with Brain MRI Findings: The ARIC Study.

Background:Increasing evidence links higher particulate matter (PM) air pollution exposure to late-life cognitive impairment. However, few studies have considered associations between direct estimates of long-term past exposures and brain MRI findings indicative of neurodegeneration or cerebrovascular disease.Objective:Our objective was to quantify the association between brain MRI findings and PM exposures approximately 5 to 20 y prior to MRI in the Atherosclerosis Risk in Communities (ARIC) study.Methods:ARIC is based in four U.S. sites: Washington County, Maryland; Minneapolis suburbs, Minnesota; Forsyth County, North Carolina; and Jackson, Mississippi. A subset of ARIC participants underwent 3T brain MRI in 2011–2013 (). We estimated mean exposures to PM with an aerodynamic diameter less than 10 or ( and ) in 1990–1998, 1999–2007, and 1990–2007 at the residential addresses of eligible participants with MRI data. We estimated site-specific associations between PM and brain MRI findings and used random-effect, inverse variance–weighted meta-analysis to combine them.Results:In pooled analyses, higher mean and exposure in all time periods were associated with smaller deep-gray brain volumes, but not other MRI markers. Higher exposures were consistently associated with smaller total and regional brain volumes in Minnesota, but not elsewhere.Conclusions:Long-term past PM exposure in was not associated with markers of cerebrovascular disease. Higher long-term past PM exposures were associated with smaller deep-gray volumes overall, and higher exposures were associated with smaller brain volumes in the Minnesota site. Further work is needed to understand the sources of heterogeneity across sites. https://doi.org/10.1289/EHP2152

Association between air pollution and mammographic breast density in the Breast Cancer Surveilance Consortium

BackgroundMammographic breast density is a well-established strong risk factor for breast cancer. The environmental contributors to geographic variation in breast density in urban and rural areas are poorly understood. We examined the association between breast density and exposure to ambient air pollutants (particulate matter <2.5 μm in diameter (PM2.5) and ozone (O3)) in a large population-based screening registry.MethodsParticipants included women undergoing mammography screening at imaging facilities within the Breast Cancer Surveillance Consortium (2001–2009). We included women aged ≥40 years with known residential zip codes before the index mammogram (n = 279,967). Breast density was assessed using the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS) four-category breast density classification. PM2.5 and O3 estimates for grids across the USA (2001–2008) were obtained from the US Environmental Protection Agency Hierarchical Bayesian Model (HBM). For the majority of women (94%), these estimates were available for the year preceding the mammogram date. Association between exposure to air pollutants and density was estimated using polytomous logistic regression, adjusting for potential confounders.ResultsWomen with extremely dense breasts had higher mean PM2.5 and lower O3 exposures than women with fatty breasts (8.97 vs. 8.66 ug/m3 and 33.70 vs. 35.82 parts per billion (ppb), respectively). In regression analysis, women with heterogeneously dense vs. scattered fibroglandular breasts were more likely to have higher exposure to PM2.5 (fourth vs. first quartile odds ratio (OR) = 1.19, 95% confidence interval (CI) 1.16 − 1.23). Women with extremely dense vs. scattered fibroglandular breasts were less likely to have higher levels of ozone exposure (fourth vs. first quartile OR = 0.80, 95% CI 0.73–0.87).ConclusionExposure to PM2.5 and O3 may in part explain geographical variation in mammographic density. Further studies are warranted to determine the causal nature of these associations.

Particulates and noise exposure during bicycle, bus and car commuting: A study in three European cities

BackgroundIn order to curb traffic-related air pollution and its impact on the physical environment, contemporary city commuters are encouraged to shift from private car use to active or public transport modes. However, personal exposures to particulate matter (PM), black carbon and noise during commuting may be substantial. Therefore, studies comparing exposures during recommended modes of transport versus car trips are needed. MethodsWe measured personal exposure to various-sized particulates, soot, and noise during commuting by bicycle, bus and car in three European cities: Helsinki in Finland, Rotterdam in the Netherlands and Thessaloniki in Greece using portable monitoring devices. We monitored commonly travelled routes in these cities. ResultsThe total number of one-way trips yielding data on any of the measured parameters were 84, 72, 94 and 69 for bicycle, bus, closed-window car and open-window car modes, respectively. The highest mean PM2.5 (85µg/m3), PM10 (131µg/m3), black carbon (10.9µg/m3) and noise (75dBA) levels were recorded on the bus, bus (again), open-window car and bicycle modes, respectively, all in Thessaloniki, PM and soot concentrations were generally higher during biking and taking a bus than during a drive in a a car with closed windows. Ratios of bike:car PM10 ranged from 1.1 in Thessaloniki to 2.6 in Helsinki, while bus:car ratios ranged from in 1.0 in Rotterdam to 5.6 in Thessaloniki. Higher noise levels were mostly recorded during bicycle rides. ConclusionBased on our study, active- and public-transport commuters are often at risk of higher air pollution and noise exposure than private car users. This should be taken into account in urban transportation planning.

Prenatal air pollution exposure and newborn blood pressure.

BackgroundAir pollution exposure has been associated with increased blood pressure in adults.Objective:We examined associations of antenatal exposure to ambient air pollution with newborn systolic blood pressure (SBP).Methods:We studied 1,131 mother–infant pairs in a Boston, Massachusetts, area pre-birth cohort. We calculated average exposures by trimester and during the 2 to 90 days before birth for temporally resolved fine particulate matter (≤ 2.5 μm; PM2.5), black carbon (BC), nitrogen oxides, nitrogen dioxide, ozone (O3), and carbon monoxide measured at stationary monitoring sites, and for spatiotemporally resolved estimates of PM2.5 and BC at the residence level. We measured SBP at a mean age of 30 ± 18 hr with an automated device. We used mixed-effects models to examine associations between air pollutant exposures and SBP, taking into account measurement circumstances; child’s birth weight; mother’s age, race/ethnicity, socioeconomic position, and third-trimester BP; and time trend. Estimates represent differences in SBP associated with an interquartile range (IQR) increase in each pollutant.Results:Higher mean PM2.5 and BC exposures during the third trimester were associated with higher SBP (e.g., 1.0 mmHg; 95% CI: 0.1, 1.8 for a 0.32-μg/m3 increase in mean 90-day residential BC). In contrast, O3 was negatively associated with SBP (e.g., –2.3 mmHg; 95% CI: –4.4, –0.2 for a 13.5-ppb increase during the 90 days before birth).Conclusions:Exposures to PM2.5 and BC in late pregnancy were positively associated with newborn SBP, whereas O3 was negatively associated with SBP. Longitudinal follow-up will enable us to assess the implications of these findings for health during later childhood and adulthood.Citation:van Rossem L, Rifas-Shiman SL, Melly SJ, Kloog I, Luttmann-Gibson H, Zanobetti A, Coull BA, Schwartz JD, Mittleman MA, Oken E, Gillman MW, Koutrakis P, Gold DR. 2015. Prenatal air pollution exposure and newborn blood pressure. Environ Health Perspect 123:353–359; http://dx.doi.org/10.1289/ehp.1307419

The Neighborhood Scale Variability of Airborne Particulates

Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.

Indoor air quality in Virginia waterpipe cafés

IntroductionA revised indoor air quality law has been implemented in Virginia to protect the public from the harmful effects of secondhand smoke exposure. This legislation contains exemptions that include allowances...

Atmospheric particulate (PM10 and PM2.5) mass concentration and seasonal variation study in the Taiwan area during 2000–2008

Hourly data of PM10 particulate concentrations collected from seven air quality monitoring stations (Banciao, Guanyin, Shalu, Erlin, Tainan, Hualien, and Taitung) in Taiwan were analyzed during the years 2000 to 2008. Hourly data of PM2.5 particulate concentrations collected from seven air quality monitoring stations (Banciao, Guanyin, Shalu, Erlin, Tainan, Hualien, and Taitung) in Taiwan were analyzed during the years 2006 to 2008. The distance between the monitoring stations and the diurnal variation of particulates PM10 and PM2.5 distribution detected at those monitoring stations were also analyzed. The mean particle concentration of PM10 was significantly higher in the Erlin and Tainan areas than all other monitoring stations in Taiwan during the monitoring period. The range of the ratios of PM2.5/PM10 mass concentrations at Banciao, Guanyin, Shalu, Erlin, Tainan, Hualien, and Taitung was 0.59–0.62, 0.45–0.51, 0.49–0.56, 0.54–0.71, 0.55–0.64, 0.56–0.61, and 0.26–0.52 during 2006 to 2008, respectively. Finally, the highest mean PM2.5 particle concentration was detected during spring at the Banciao, Hualien, and Taitung air quality monitoring stations in Taiwan during the monitoring period. The highest mean PM2.5 particle concentration was recorded during winter at the Guanyin, Shalu, Erlin, and Tainan air quality monitoring stations during the years of 2000–2008.