High PM2 Research Articles

Disparities in precipitation effects on PM2.5 mass concentrations and chemical compositions: Insights from online monitoring data in Chengdu

Precipitation plays a pivotal role in wet deposition, significantly affecting aerosol purification. The efficacy of precipitation in removing aerosols depends on its type and the characteristics of the particulates involved. However, further research is necessary to fully understand how precipitation impacts PM2.5 components. This study utilized high-temporal-resolution data on PM2.5, its components and meteorological factors to examine varying responses influenced by precipitation intensity and duration. The findings indicate that increased rainfall intensity and duration enhance PM2.5 and its constituents removal efficiency. Specifically, longer precipitation periods significantly improve PM2.5 purification, especially with drizzle and light rain. Moreover, there is a direct correlation between pre-precipitation PM2.5 levels and its scavenging rates, with drizzle potentially exacerbating PM2.5 pollution under cleaner conditions (≤ 35 µg/m3). Seasonally, the efficacy of removing PM2.5 components varies notably in response to drizzle and light rain. In spring, higher PM2.5 levels after drizzle were primarily due to increased organic carbon concentrations favored by higher relative humidity and lower pH conditions compared to other seasons, conducive to secondary organic aerosol production. Lower wind speeds and higher temperatures further contribute to water-soluble organic carbon accumulation. Daytime and nighttime precipitation exerted differing influences on PM2.5 components, particularly in spring where daytime drizzle and light rain significantly increased PM2.5 and its constituents, notably NO3-, potentially associated with phase distribution changes between gas and aerosol phases in low-temperature, high-RH conditions compared to nighttime. These results propose a dual-impact mechanism of precipitation on PM2.5 and provide scientific basis for designing effective control strategies.

Adverse effects of meteorological factors and air pollutants on dry eye disease: a hospital-based retrospective cohort study

Although previous studies have suggested that meteorological factors and air pollutants can cause dry eye disease (DED), few clinical cohort studies have determined the individual and combined effects of these factors on DED. We investigated the effects of meteorological factors (humidity and temperature) and air pollutants [particles with a diameter ≤ 2.5 μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}m (PM2.5), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO)] on DED. A retrospective cohort study was conducted on 53 DED patients. DED was evaluated by Symptom Assessment in Dry Eye (SANDE), tear secretion, tear film break-up time (TBUT), ocular staining score (OSS), and tear osmolarity. To explore the individual, non-linear, and joint associations between meteorological factors, air pollutants, and DED parameters, we used generalized linear mixed model (GLMM) and Bayesian kernel machine regression (BKMR). After adjusting for all covariates, lower relative humidity or temperature was associated with a higher SANDE (p < 0.05). Higher PM2.5, O3, and NO2 levels were associated with higher SANDE and tear osmolarity (p < 0.05). Higher O3 levels were associated with lower tear secretion and TBUT, whereas higher NO2 levels were associated with higher OSS (p < 0.05). BKMR analyses indicated that a mixture of meteorological factors and air pollutants was significantly associated with increased SANDE, OSS, tear osmolarity, and decreased tear secretion.

Multiobjective Optimization of High Power Density Outer Rotor PM Starter Generator Considering Electromagnetic and Temperature Characteristics

Multiobjective Optimization of High Power Density Outer Rotor PM Starter Generator Considering Electromagnetic and Temperature Characteristics

Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017)

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Improvement of Air Quality During Lockdown Period of COVID-19 Pandemic in Kathmandu Valley, Nepal

The Kathmandu Valley is currently Nepal's most hazardous region for human health and environmental sustainability because of increased air pollution and high particulate matter levels. According to multiple studies and observations, Nepal's urban districts saw an improvement in air quality during the COVID-19 lockdown. The Air Quality Index (AQI) is a standard method of informing the public about air pollution levels. The AQI is based on the quantitative value of pollutants' constituent parts, such as particulate matter (PM). Thus, the focus of this study was on how the AQI values change in the Ratna Park suburb of the Kathmandu district in the Kathmandu Valley and how those fluctuations are related to lockdown and non-lockdown times during the COVID-19 pandemic and comparing them to identify crucial information’s. This study shows that during the lockdown, the air quality in the Kathmandu Valley dramatically improved, with average AQI values for PM2.5 and PM10 rising to 21.34 and 64.42 from 50.74 and 106.51, respectively. Furthermore, we investigate the positive correlation between relative humidity, a meteorological component, and air quality.

Implementation of Indoor Air Quality Monitoring Systems of Particulate Matter 2.5 Based on the Internet of Things

Introduction: Indoor air quality significantly influences human health because humans can do work and rest indoors. Particulate Matter (PM2.5) is ranked 5th as the leading risk factor for death in the world and causes more than 103 million disabilities. PM2.5 concentrations are the highest, so a monitoring system is needed that can monitor air conditions in real-time and continuously. Methods: Research was conducted using the Research and Development (R&amp;D) type. The scope of the research method is making prototypes and fieldtesting tools. Determination of field test locations is based on indicators such as (i) inadequate ventilation in the respondent's house so that air circulation does not run well; (ii) The family room and bedroom are close to the kitchen and waste incinerator, which has the potential to produce PM2.5 pollutants. Data analysis used descriptive analysis to get a picture of indoor air quality. Results and Discussion: The designed monitoring system was calibrated with the Air Quality Monitor 8 in 1 PM2.5 tool. The average PM2.5 concentration measurement for 24 hours showed that all test points exceeded the standard limit. Room 3 (family room) had the highest PM2.5 concentration, while room 4 (bedroom) had the lowest PM2.5 concentration. Conclusion: The area of air ventilation, mining activities, mining product transportation activities, the intensity of watering roadsides and home yards, and cooking activities influence PM2.5 concentrations.

Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan plateau with data-driven approach

The Tibetan Plateau, known as the “Third Pole”, is susceptible to ground-level ozone (O3) and fine particulate matter (PM2.5) pollution due to its unique high-altitude environment. This study constructed random forest regression models using multi-source data from ground measurements and meteorological satellites to predict variations in ground-level O3 and PM2.5 concentrations and their influencing factors across seven major cities in the Tibetan Plateau over two-year periods. The models successfully reproduced O3 and PM2.5 levels with satisfactory R-squared values of 0.71 and 0.73, respectively. Results reveal combustion-related carbon monoxide (CO) and nitrogen dioxide (NO2) as the most substantial influences on O3 and PM2.5 concentrations. Solar radiation, geographical factors, and meteorological variables also played crucial roles in driving pollutant variations. Conversely, transport-related and human activity factors exhibited relatively lower significance. High O3 and PM2.5 pollution occurred during pre-monsoon and post-monsoon/winter seasons, driven by solar radiation and emissions, respectively. While CO consistently contributed across cities and seasons, key influencing factors varied locally. This study unveils the key driving forces governing air pollutant variations across the Tibetan Plateau, shedding light on complex atmospheric processes in this unique high-altitude region.

Time series analysis of the interaction between ambient temperature and air pollution on hospitalizations for AECOPD in Ganzhou, China

This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure–response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO2, PM2.5, and PM10 were − 0.428 (95% CI − 0.637, − 0.218), -− 0.227 (95% CI − 0.293, − 0.161), and − 0.119 (95% CI − 0.159, − 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO2 (> 33 μg/m3) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM10 in men and high PM2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

The association between airborne particulate matter (PM2.5) exposure level and primary open-angle glaucoma

The eye is vulnerable to the adverse effects of air pollution. Previous experimental study found that fine particulate matter (PM2.5) had a direct toxic effect on intraocular tissues. However, clinical evidence for the impact of air pollutants exposure on functional and structural changes in glaucoma remains scarce. A total of 120 patients with primary open-angle glaucoma (POAG) who met the inclusion criteria were included in this retrospective study. The standardized ophthalmic examination, such as intraocular pressure (IOP), visual field, optical coherence tomography, and comprehensive physical examination, were performed. The air pollution data, including PM2.5 concentration and air quality index (AQI), were collected. PM2.5 and AQI for the day of the medical examination, as well as one month, and three months before the medical examination date, were investigated. In our results, higher average exposure levels for one-month and three-month, were associated with increased IOP (r=0.229, P=0.013; r=0.204, P=0.028, respectively) and decreased visual field mean sensitivity (MS) (r=-0.212, P=0.037; r=-0.305, P=0.002, respectively). PM2.5 concentrations for the day of the medical examination was not significantly associated with ocular parameters. In multiple linear regression analysis adjusted for demographic and clinical factors, higher PM2.5 exposure for one month was associated with elevated IOP (P=0.040, β=0.173, 95 %CI=0.008–0.337). We also found an association between PM2.5 and MS (one-month exposure: β=-0.160, P=0.029; three-month exposure: β=-0.238, P=0.002). The logistic regression analysis found that three-month average PM2.5 exposure level was significantly associated with the disease severity (β=0.043, P=0.025, 95 %CI=1.005–1.084). In conclusion, this study is the first to investigate the relationship between air pollution and detailed ocular parameters of POAG patients in Shanghai over a three-year period, and to explore the effects of different exposure times of PM2.5 on glaucoma. This study found that PM2.5 exposure was correlated with elevated IOP and decreased MS. The one-month PM2.5 exposure level had the most significant effects on IOP. The three-month PM2.5 exposure level was an independent risk factor for POAG severity. Current evidence suggests there may be an association between PM2.5 exposure and POAG.

Joint Effect of Short-Term Exposure to Fine Particulate Matter and Ozone on Mortality: A Time Series Study in 272 Chinese Cities.

Short-term exposure to PM2.5 or O3 can increase mortality risk; however, limited studies have evaluated their interaction. A multicity time series study was conducted to investigate the synergistic effect of PM2.5 and O3 on mortality in China, using mortality data and high-resolution pollutant predictions from 272 cities in 2013-2015. Generalized additive models were applied to estimate associations of PM2.5 and O3 with mortality. Modification and interaction effects were explored by stratified analyses and synergistic indexes. Deaths attributable to PM2.5 and O3 were evaluated with or without modification of the other pollutant. The risk of total nonaccidental mortality increased by 0.70% for each 10 μg/m3 increase in PM2.5 when O3 levels were high, compared to 0.12% at low O3 levels. The effect of O3 on total nonaccidental mortality at high PM2.5 levels (1.26%) was also significantly higher than that at low PM2.5 levels (0.59%). Similar patterns were observed for cardiovascular or respiratory diseases. The relative excess risk of interaction and synergy index of PM2.5 and O3 on nonaccidental mortality were 0.69% and 1.31 with statistical significance, respectively. Nonaccidental deaths attributable to short-term exposure of PM2.5 or O3 when considering modification of the other pollutant were 28% and 31% higher than those without considering modification, respectively. Our results found synergistic effects of short-term coexposure to PM2.5 and O3 on mortality and suggested underestimations of attributable risks without considering their synergistic effects.

Toxic elements, sources and health risk assessment of PM2.5 in an industrial area of Surabaya, Indonesia

ABSTRACT A comprehensive study on air quality in the industrial area of Indonesia, which significantly impacts respiratory health and poses breathing hazards, is still lacking. This study aims to uncover the contents of toxic elements, identify potential sources of fine particles, and evaluate their contributions to health risks in Surabaya’s industrial area. The study focuses on identifying chemical composition, sources, and assessing health risks for both children and adults. The PM2.5 concentration from 2021 to 2022 ranged from 3.29 to 27.05 µg m−3, with an average value of 13.63 ± 4.54 µg m−3, exceeding the WHO’s annual standard of 5 µg m−3. Black carbon (BC) accounted for 27% of PM2.5 composition. A seasonal variation was found; the highest PM2.5 and BC were observed during the dry season, with the highest peak in June. The highest concentration of elements in PM2.5 was observed for S, followed by Si, K, Zn, Fe, and Pb, with maximum values of 3.53, 1.16, 1.01, 1.86, 0.29, and 0.37 µg m−3, respectively. Source apportionment using positive matrix factorisation (PMF) revealed eight sources: galvanising industry, ammonium chloride, secondary sulphate, biomass burning emission, soil, steel industry, vehicle emissions, and lead smelting, with metal industries accounting for 37.3%. Investigation into their potential source locations was conducted using the conditional bivariate probability function (CBPF) method. Human health assessment for adults and children indicates carcinogenic risks with a total risk of 9.50 × 10−5 and 2.38 × 10−5, respectively. The results emphasise the significance of managing sources linked to industry and transportation-related sources, crucial in decreasing high PM2.5 levels and several elevated elements and mitigating the health impact in Surabaya’s industrial area, Indonesia.

Drivers of PM2.5 Episodes and Exceedance in India: A Synthesis From the COALESCE Network

AbstractEmission sources influencing high particulate air pollution levels and related mortality in India have been studied earlier on country‐wide and sub‐national scales. Here, we use novel data sets of emissions (for 2019) and observations created under the Carbonaceous Aerosol Emissions, Source Apportionment, and Climate Impacts network in India (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1) in WRF‐Chem simulations to evaluate drivers of high PM2.5 levels during episodes and in airsheds with different pollution levels. We identify airsheds in “extreme” (110–140 μg/m3), “severe” (80–110 μg/m3) and “significant” (40–80 μg/m3) exceedance of the Indian annual ambient air quality standard (National Ambient Air Quality Standards [NAAQS]) of 40 μg/m3 for PM2.5. We find that primary organic matter and anthropogenic mineral matter (largely coal fly‐ash) drive high PM2.5 levels, both annually and during high PM2.5 episodes. PM2.5 episodes are driven by organic aerosol in north India (Mohali) in wintertime but are additionally influenced by mineral matter and secondary inorganics in central (Bhopal), south India (Mysuru) and eastern India (Shyamnagar). Across airsheds in exceedance of the NAAQS and during high PM2.5 episodes, primary PM2.5 emissions arise largely from the residential sector (50%–75%). Formal sector emissions (industry, thermal power and transport; 40%–55%) drive airshed and episode scale PM2.5 exceedance in northern and eastern India. Agricultural residue burning emissions predominate (50%–75%) on episode scales, both in northern and central India, but not on annual scales. Interestingly, residential sector emissions strongly influence (60%–90%) airsheds in compliance with the NAAQS (annual mean PM2.5 &lt; 40 μg/m3), implying the need for modern residential energy transitions for the reduction of ambient air pollution across India.

Associations of prenatal exposure to PM2.5 and its components with offsprings' neurodevelopmental and behavioral problems: A prospective cohort study from China

Prenatal exposure to fine particulate matter (PM2.5) has been linked with increased neurodevelopmental disorders. However, the most detrimental component of PM2.5 and the most vulnerable exposure time windows remain undetermined, especially in areas with high PM2.5 levels. In a prospective cohort study involving 4494 mother-child dyads, we examined the associations of prenatal exposure to PM2.5 and its four main components with children's neurodevelopmental and behavioral problems (NBPs), separately in three pregnancy trimesters. Poisson regression and generalized additive models were used to depict the linear and nonlinear associations, respectively. Weighted quantile sum and Bayesian kernel machine regression models were applied to examine the effects of exposure to both mixed and individual components. Results showed that exposure to PM2.5 and its components throughout the three trimesters increased the risk of children's NBPs (Risk ratio for PM2.5: 1.16, 95 % confidence interval 1.14–1.18 per μg/m3 in the first trimester; 1.15, 1.12–1.17 in the second trimester; 1.06, 1.04–1.08 in the third trimester), with associations gradually diminishing as pregnancy progressed (P values for trends < 0.05). Among the four main components of PM2.5, exposure to SO42- posed the highest risks on children's NBPs, while organic matter contributed the largest proportion to the overall impacts of PM2.5 exposure. These results underscore the significance of mitigating PM2.5 exposure in pregnant women to reduce the risk of neurodevelopmental disorders in offspring. Our findings would inform risk assessment of PM2.5 exposure and facilitate the development of precision preventive strategies targeting specific components of PM2.5 in similar areas with high levels of exposure.

Morphology, aspect ratio, and surface elemental composition of primary aerosol particles at urban region of India.

The PM2.5 and PM10 particles were characterized in terms of morphology (size and shape) and surface elemental composition at two different (traffic and industrial) locations in urban region of India and further linked to different morphological defining parameters. The overall PM2.5 and PM10 showed significant daily variability indicating higher PM10 as compared to PM2.5. PM2.5/PM10 ratio was found to be 0.58 ± 0.10 indicating the abundance of PM2.5. Soot aggregates, aluminosilicates, and brochosomes particles were classified based on morphology, aspect ratio (AR), and surface elemental composition of single particles. The linear regression analysis indicates the significant correlation between area equivalent (Daeq) and feret diameter (Dfd) (R2 0.86-0.98). Higher aspect ratio (1.48 ± 0.87-1.43 ± 0.50) was noted at traffic site as compared to industrial site (1.33 ± 0.58-1.29 ± 0.30), while circularity showed the opposite trend. Fractal dimension (Df) of soot aggregates estimated by the soot parameters method (SPM) were found to be 1.70, 1.72, and 1.88, mainly attributed to vehicular emissions, biomass, and industrial emission/coal burning, respectively. This further inferred that freshly emitted soot particles exhibited lacey in nature with spherical shape (Df 1.70) at traffic site, while at industrial location, they were different with compact shapes (Df 1.88) due to particle aging processes. This study inferred the synoptic changes in mass, chemical characteristics, and morphology of aerosol particles which provide the new insights into individual atmospheric particle and their dynamic nature.

Characterization of the Gluten Aggregation, Dough Rheological, and Physicochemical Properties of Flours Used in the Production of Flat Breads in Eskişehir

In this study, the physical, chemical, technological, gluten aggregation, and dough rheological properties of flat bread flours (“Bazlama", "Pide", "Gözleme", "Lavash") produced in Eskişehir were evaluated in detail by comparing them with “Bread” flour. The highest SRC lactic acid and GPI (gluten performance indeks) values (118.1% and 0.648, respectively) were found in "Gözleme" flour; however, "Lavash" and "Pide" flours had values close to “Bread” flour. The lowest SRC lactic acid and GPI values were obtained in "Bazlama" flour (84.13% and 0.531, respectively). The protein content of “Lavash” flour was close to bread flour (10.60%), while the protein content of "Pide" and "Gözleme" flours was higher (11.07% and 10.94%, respectively). The protein content of “Bazlama” flour was significantly lower than that of other flours (9.84%). Again, the sedimentation value of “Gözleme” flour was above that of other flours (65.0 ml). Lavash flour also had a high sedimentation value (60.0 ml). The sedimentation values of “Pide” and “Bread” flour were similar (approximately 53.0 ml) and lower. “Bazlama” flour gave a low sedimentation value (43.0 ml). Unlike bread flour, flat bread flours had a gluten indeks (GI) value higher than 90.0%. The ash content of flat bread flours varied between approximately 0.550 and 0.650%. “Bread” flour had the longest GlutoPeak peak maximum time (PMT) (102.0 s). “Bazlama” flour had the lowest torque 15 s before the maximum torque (BM), maximum torque (BEM), torque 15 s after the maximum torque (PM), energy, and water absorption (19,3 BU; 36,3 BU; 22,0 BU; %9,07; %18,57, 26,0 J ve %53,5, respectively). The BEM value of “Lavash”, “Gözleme” and “Pide” flours was higher than bread flour (around 45 BU). Of these three flours, “Pide” and “Lavash” flours maintained their high BEM after 15 s and gave a high PM (37.7 BU and 31.7 BU, respectively). “Bazlama” flour had the lowest PM value (22.0 BU). Particularly, “Gözleme” and “Pide” flours had high gluten-aggregation properties. Although the gluten and protein content values of “Lavash” flour were similar to those of “Bread” flour, its energy and water absorption values were higher (127.3 J and 58.0%). The difference between “Lavash” and “Pide” flours was a short PMT value (around 50 s). “Gözleme” flour had the highest stability (4.15 min.) and the lowest softening degree (49.5 FU) in the farinograph. Farinograph stability, development time, and softening degree values were also better in “Pide” flour (3.05 min., 2.85 min., and 85.0 BU, respectively). The farinograph qulaity number (FQN) values of these two flours were also high (65.5 and 50.0). “Bazlama” flour had low stability, FQN, and high softening degree. The “Bazlama” flour had the highest softening value (243.5 FU). Farinograph rheological properties of “Lavash” and control “Bread” flour were similar to each other. While the G' value of “Gözleme” and “Pide” flours, which were more stable in the farinograph, was found to be higher (27200 Pa and 24525 Pa), the G' value of “Lavash” flour was the lowest (around 12215 Pa). The G' values of “Bazlama” and “Bread” flour were similar (18050 Pa and 16590 Pa). The “Gözleme” and “Pide” flours with higher storage resistance (G') had higher G'' values (around 12000 Pa). “Lavash” flour had the lowest G'' value (6433.5 Pa). The lowest G'' values of “Bazlama” and “Bread” flour were found to be similar. “Gözleme” flour had the lowest Tanδ value (0.464). It is also low in “Pide” flour (0.491). The dough from these flours had an elastic structure. The “Lavash” and “Bazlama” flours had the highest Tanδ value (0.531 and 0.537). As a result, “Lavash” and “Bazlama” flours were found to have weaker and more extensible viscoelastic properties.

Efficiency evaluation of a lab-scale photoelectric precipitator for particulate matter emission reduction.

The importance of studying particulate matter lies in its detrimental impact on human health and the environment. Industrial emissions often carry substantial dust content, necessitating the reduction of their environmental release. This study introduced a laboratory-scale photoelectric precipitator to assess its effectiveness in curbing particle emissions under varying temperature, humidity, and residence time conditions. This device operates in two stages: firstly, it charges particles by exposing copper wire surfaces to ultraviolet rays, generating photoelectrons in the airflow; secondly, it utilizes a positively charged collector surface for absorption and collection. Assessment under different temperature, residence time, and humidity conditions revealed that the system designed for 10μm diameter particles displayed the highest efficiency. At 150℃, the removal efficiency was 39.55%, rising to 41.34% at 60% humidity and 43.58% with an 18-second residence time. Furthermore, increasing energy consumption from 144 j/l to 720 j/l resulted in a 10.93% efficiency increase, highlighting the correlation between energy input and system efficiency. High particulate matter levels diminish visibility, harm the climate, ecosystems, materials, and contribute to respiratory and cardiovascular ailments. These findings underline the photoelectric precipitator's potential in mitigating particulate matter's adverse effects on health and the environment. However, further research is warranted to optimize system design and explore additional parameters' impact on performance, ensuring its effectiveness in industrial processes to reduce particulate matter emissions.

Quantitative analysis of aeolian dust incidence and diagnosis using a dynamic Bayesian network model: A case study of estuary area in central Taiwan

The presence of high concentrations of PM10 in aeolian dust within the estuary area of central Taiwan has been a focal point for air quality protection and remediation efforts over the past 15 years. This study explores the quantitative analysis of aeolian dust occurrences concerning various levels of PM10 concentration, aiming to offer early warning information to local communities and government agencies. Employing Dynamic Bayesian network (DBN) models, the research interrelates conditional probability dependencies among air quality and meteorological factors monitored in the downstream area of the Jhuoshuei River, predicting the probability distribution of aeolian dust events. The constructed DBNs demonstrate the inference of probability distributions of meteorological factors' states based on PM10 concentrations, enabling diagnostic analysis of meteorological conditions during high PM10 concentration dust events. The DBN models exhibit robust predictive ability, achieving an overall accuracy of 85.72% in validation and 85.8% in testing, with high discriminatory capacity that the area under the receiver operating characteristic curve (AUC) values of 0.924 and 0.915 during validation and testing, respectively. Sensitivity analysis highlights wind direction, wind speed, relative humidity as significant factors affecting PM10 concentrations. The probability of low PM10 concentrations was notably higher compared to higher concentrations, indicating effective aeolian dust remediation efforts. However, attention is warranted toward the 10% incidence of aeolian dust events deemed unhealthy or hazardous to human health. Diagnosis of meteorological conditions during high PM10 concentration events suggests low temperature, high wind speeds, and dry conditions, particularly with northeastern or southwestern winds, as conducive factors. This study underscores the physical relationship between meteorological factors and PM10 concentrations, employing DBNs to consider temporal relationships and uncertainties in data, aligning with human health impact standards proposed by the Ministry of Environment. Future research should incorporate soil and land surface conditions to provide a more comprehensive understanding of aeolian dust dynamics and mitigation strategies.

Assessment of PM2.5 Concentration at University Transit Bus Stops Using Low-Cost Aerosol Monitors by Student Commuters.

Particulate matter of 2.5 µm and smaller (PM2.5) is known to cause many respiratory health problems, such as asthma and heart disease. A primary source of PM2.5 is emissions from cars, trucks, and buses. Emissions from university transit bus systems could create zones of high PM2.5 concentration at their bus stops. This work recruited seven university students who regularly utilized the transit system to use a low-cost personal aerosol monitor (AirBeam) each time they arrived at a campus bus stop. Each participant measured PM2.5 concentrations every time they were at a transit-served bus stop over four weeks. PM2.5 concentration data from the AirBeam were compared with an ADR-1500 high-cost monitor and EPA PM2.5 reference measurements. This methodology allowed for identifying higher-than-average concentration zones at the transit bus stops compared to average measurements for the county. By increasing access to microenvironmental data, this project can contribute to public health efforts of personal protection and prevention by allowing individuals to measure and understand their exposure to PM2.5 at the bus stop. This work can also aid commuters, especially those with pre-existing conditions who use public transportation, in making more informed health decisions and better protecting themselves against new or worsening respiratory conditions.

Response of low-cost sensors to high PM 2.5 concentrations during bushfire and haze events

Response of low-cost sensors to high PM _2.5 concentrations during bushfire and haze events

Impact of ventilation system retrofitting on indoor air quality in a single-family building

Due to prolonged human occupancy within indoor environments, the quality of indoor air holds significant importance. This study evaluates the impact of retrofitting a ventilation system from natural to mechanical on indoor air quality (IAQ) in a bedroom during heating seasons in 2017/18 and 2020/21. Measurements were conducted before and after modernization to assess CO2 concentrations, temperature, humidity, particulate matter (PM) and bacterial and fungal aerosols levels indoors and outdoors. Results show a significant decrease in CO2 concentration (from 1000 ± 240 ppm to 806 ± 165 ppm) and relative humidity (from 53.0 ± 3.6 % to 28.2 ± 4.1 %) post-modernization. Notable reduction in CO2 concentrations averaging 20 % was recorded, thereby elevating air quality category from IDAIII to IDAII. Additionally, the study determined the infiltration coefficient for PM2.5, which increased from 0.48 under natural ventilation to 0.63 after retrofitting. PM2.5 concentrations increased indoors (from 23.49 ± 11.21 μg/m3 to 56.68 ± 13.47 μg/m3) due to higher infiltration rates with mechanical ventilation. Bioaerosol measurements indicate fluctuating concentrations, with bacterial aerosols peaking in the evening. The use of mechanical ventilation led to increased transport and dispersion of bacterial bioaerosols indoors. In contrast, this was not observed for fungal aerosols. Additionally, the study highlights limitations in ventilation filtration efficiency and recommends the installation of PM2.5 sensors to regulate airflow during periods of high outdoor PM2.5 concentrations. Overall, while mechanical ventilation improved some IAQ parameters, it posed challenges in controlling particulate matter infiltration, emphasizing the need for comprehensive ventilation strategies to ensure indoor air quality and occupants' well-being.