Particulate Matter Mitigation Research Articles

A wearable real-time particulate monitor demonstrates that soaking hay reduces dust exposure.

Affordable particulate matter (PM) monitors suitable for use on horses will facilitate the evaluation of PM mitigation methods and improve the management of equine asthma. Calibrate a real-time wearable PM monitor (Black Beauty [BB]) and compare the PM exposures of horses fed dry or soaked hay. Laboratory calibration; complete cross-over feed trial. Side-by-side sampling with BB monitors and tapered element oscillating microbalances (TEOMs) was performed under varying concentrations of PM from alfalfa hay. Linear regression was used to derive a calibration formula for each unit based on TEOM PM measurements. Precision was evaluated by calculating the coefficient of variation and pairwise correlation coefficients between three BB monitors. PM exposure was measured at the breathing zone of 10 horses for 8 h after they were fed dry or soaked hay. Repeated measures generalised linear models were constructed to determine the effect of hay treatment and measurement duration (initial 20-min vs. 8-h) upon exposure to PM with diameters smaller than or equal to 10 μm (PM10) and 2.5 μm (PM2.5). BB monitor PM2.5 and PM10 measurements were linearly correlated with TEOM data (coefficient of determination r2 > 0.85 and r2 > 0.90 respectively), but underestimated PM2.5 mass concentrations by a factor of 4 and PM10 concentrations by a factor of 44. Measures from the three BB monitors had a coefficient of variation <15% and pairwise r > 0.98. Feeding soaked hay significantly reduced average PM2.5 exposures (20-min: dry: 160 μg/m3, soaked: 53 μg/m3, p < 0.0001; 8-h: dry: 76 μg/m3, soaked: 31 μg/m3, p = 0.0008) and PM10 exposures (20-min: dry: 2829 μg/m3, soaked: 970 μg/m3, p < 0.0001; 8-h: dry: 1581 μg/m3, soaked: 488 μg/m3, p = 0.008). No health outcome measures were collected. With appropriate corrections, the BB monitor can be used to estimate horse PM exposure. While 20-min measurements yielded higher estimates of exposure than 8-h measurements, both intervals demonstrate that soaking hay reduces PM exposures by more than 50%.

Quantification of Airborne Particulate Matter and Trace Element Deposition on Hedera helix and Senecio cineraria Leaves.

In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive sampling. Furthermore, these particles are often enriched with potentially toxic trace elements and organic pollutants. This study evaluates both the composition of atmospheric dust accumulated during a certain timespan on Hedera helix and Senecio cineraria leaves and the potential for their use as bio-monitors. The test plants were positioned near automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. The gravimetric deposition of PM10 and PM2.5 on leaves was compared with data recorded by the monitoring stations and related to the weather conditions reported by Argentina's National Meteorological Service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied: XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2) did PM10 and PM2.5 concentrations (90 µg m-3 and 9 µg m-3) in the air exceed more than five times WHO guidelines (15 µg m-3 and 5 µg m-3). However, several trace elements were found to be enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated with construction, traffic and unpaved street sources. Furthermore, based on its capability to sequester above 2800 µg cm-2 of PM10, 2450 µg cm-2 of PM2.5 and trace elements, Senecio cineraria can be taken into consideration for adoption as a bio-monitor or even for PM mitigation.

Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles

The size and composition of particulate matter (PM) are pivotal in determining its adverse health effects. It is important to understand PM's retention by plants to facilitate its atmospheric removal. However, the distinctions between the size and composition of naturally fallen PM (NFPM) and leaf-deposited PM (LDPM) are not well-documented. Here we utilize a single-particle aerosol mass spectrometer, coupled with a PM resuspension chamber, to analyze these differences. We find that LDPM particles are 6.8–97.3% larger than NFPM. Employing a neural network algorithm based on adaptive resonance theory, we have identified distinct compositional profiles: NFPM predominantly consists of organic carbon (OC; 31.2%) and potassium-rich components (19.1%), whereas LDPM are largely composed of crustal species (53.9–60.6%). Interestingly, coniferous species retain higher OC content (11.5–13.7%) compared to broad-leaved species (0.5–1.2%), while the levoglucosan content exhibit an opposite trend. Our study highlights the active role of tree leaves in modifying PM composition beyond mere passive capture, advocating for a strategic approach to species selection in urban greening initiatives to enhance PM mitigation. These insights provide guidance for urban planners and environmentalists in implementing nature-based solutions to improve urban air quality.

Effects of urban vegetation on PM mitigation: The case of a street in Novi Sad, Serbia

Experiencing rapid development and growth, cities worldwide face a surge in air pollution, primarily driven by the increased concentrations of the particulate matter (PM) originating from various anthropogenic sources, such as traffic, household fuel combustion, and industrial and construction activities. Urban green spaces can naturally filter PM through physicochemical processes, serving as effective urban planning instruments for the improvement of the air quality. Focusing on a street in Novi Sad, the second-largest city in Serbia, this study investigates the efficiency of vegetation in mitigating air pollution, specifically PM10 emissions from traffic and construction activities. Using the contemporary monitoring and modelling techniques for measuring and predicting PM10 concentrations, the focus of this research is to evaluate the efficacy of vegetation in affecting and minimizing detected PM concentrations. The results indicate a significant reduction in the monitored PM10 concentrations behind the green barrier compared to the modelled concentrations near the pollution source (on the road) for both traffic and construction-related emissions. The paper highlights the capacity of green elements to act as natural air pollution mitigators and suggests better integration of strategic environmental management into urban planning to foster the development of healthier and more sustainable cities, providing recommendations to facilitate this objective.

Indoor air quality improvement with filtration and UV-C on mitigation of particulate matter and airborne bacteria: Monitoring and modeling

Indoor air, especially with suspended particulate matter (PM), can be a carrier of airborne infectious pathogens. Without sufficient ventilation, airborne infectious diseases can be transmitted from one person to another. Indoor air quality (IAQ) significantly impacts people's daily lives as people spend 90% of their time indoors. An industrial-grade air cleaner prototype (filtration + ultraviolet light) was previously upgraded to clean indoor air to improve IAQ on two metrics: particulate matter (PM) and viable airborne bacteria. Previous experiments were conducted to test its removal efficiency on PM and airborne bacteria between the inlet and treated air. However, the longer-term improvement on IAQ would be more informative. Therefore, this research focused on quantifying longer-term improvement in a testing environment (poultry facility) loaded with high and variable PM and airborne bacteria concentrations. A 25-day experiment was conducted to treat indoor air using an air cleaner prototype with intermittent ON and OFF days in which PM and viable airborne bacteria were measured to quantify the treatment effect. The results showed an average of 55% reduction of total suspended particulate (TSP) concentration between OFF days (110 μg/m3) and ON days (49 μg/m3). An average of 47% reduction of total airborne viable bacteria concentrations was achieved between OFF days (∼3200 CFU/m3) and ON days (∼2000 CFU/m3). A cross-validation (CV) model was established to predict PM concentrations with five input variables, including the status of the air cleaner, time (h), ambient temperature, indoor relative humidity, and day of the week to help simulate the air-cleaning effect of this prototype. The model can approximately predict the air quality trend, and future improvements may be made to improve its accuracy.

Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India.

Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.

The Role of Spontaneous Flora in the Mitigation of Particulate Matter from Traffic Roads in an Urbanised Area

Particulate matter (PM) is a serious air pollutant that poses significant health risks. One solution to reduce PM concentrations in these areas is through phytoremediation, a process that involves using plants to remove contaminants from the environment. In this study, we investigated the capacity of spontaneous flora—herbaceous plants, shrubs, and trees growing in five zones from the road—to absorb PM on their foliage. The study found significant differences in the accumulation of PM, with the highest PM accumulation recorded in Zone V, which boasted a blend of the three mentioned vegetation types together. In contrast, Zones I and II, which were located close to the road and comprised solely herbaceous plants, exhibited 14.3% and 43.4% less PM accumulation, respectively. Similarly, Zone IV, with a mix of herbaceous plants and shrubs, showed 64.5% less PM accumulation, while Zone III, with only herbaceous plants, had a staggering PM accumulation reduction of 76.8%. The sum of Si+Al+Ca displayed a similar pattern. Furthermore, the findings highlighted the valuable role of plants in decreasing PM concentrations in the air, resulting in reductions of 76%, 39%, and 47% for PM10, PM2.5, and PM1.0, respectively. The results indicate that various spontaneous flora can work in unison to reduce PM, providing a multifaceted approach to combating air pollution.

Particulate matter source attribution and restaurant mitigation behavioral intentions: an application of attribution theory

PurposeAs commercial cooking is known as a source that generates great concentrations of particulate matter (PM) emissions first accumulating in kitchens before spreading to dining areas, this study aims to explore how to improve restaurants’ efforts to reduce PM emissions by the application of attribution theory.Design/methodology/approachData were obtained from restaurant managers operating their business in South Korea, considered to be qualified to provide accurate information regarding the survey questions. A scenario-based experimental approach was used to test the hypothesized relationships. Cognitive and emotional risk judgements were assessed for its potential interaction effects on the relationships between restaurant perceptions of PM source attributions, preventions attitudes and mitigation behavioral intentions.FindingsResults revealed that perceptions of PM main sources were attributed to internal rather than external factors, which improved mitigation behavioral intentions. Such an effect was partially mediated through PM pollution prevention attitudes. Additionally, when applying external source attributions, PM mitigation behavioral intentions were improved by cognitive risk judgements, and PM prevention attitudes were enhanced by affective risk judgements.Research limitations/implicationsResults assist restaurants to better understand their operations that may be emitting significant levels of PM, thereby encouraging them to set more ambitious and effective PM mitigation operational guidelines for their employees and diners.Originality/valueThis study provides a fundamental baseline of management perceptions regarding PM emissions related to restaurant mitigation behavioral intentions. Results are useful in designing appropriate communication strategies addressing restaurant PM pollution issues to improve internal restaurant practices regarding clean air quality.

Searching for New Human Behavior Model in the Climate Change Age: Analyzing the Impact of Risk Perception and Government Factors on Intention-Action Consistency in Particulate Matter Mitigation.

This study aims to analyze factors influencing citizens’ intentions to take protective action against particulate matter (PM) and their actual actions in response to PM. There were few research on the role of government factors and the issue of intention–action inconsistency in the context of PM mitigation action. Therefore, this study set not only variables in the risk perception paradigm but also ones in government factors as independent variables, while intention and action in response to PM were set as dependent variables. This study’s analysis was based on survey data collected from Korean people. For representativeness of the samples, this study adopted the quota sampling method, considering region, gender, and age. Five hundred respondents finished the survey. To verify the hypotheses, this study used regression and binomial logistic analysis. Analysis showed that (1) negative emotions, trust, knowledge, government competency, policy satisfaction, and policy awareness had significant effects on intention and action in response to PM, and (2) perceived benefits only affected intention, whereas government accountability only affected action. Logistic analysis showed that there were groups in which intentions and actions did not match. Negative emotions and government competence induce intention–action consistency, whereas the perceived benefits and trust in government tend to encourage inconsistency. Knowledge is a variable that induces both consistency and inconsistency in the intention–action relationship. The determinant structures of independent variables affecting the likelihood of belonging to the four groups differed.

Particulate Matter in Swine Barns: A Comprehensive Review

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Due to the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. As PM in swine barns is primarily of biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

A Study of the Correlation between the Concentration of Particulate Matter and Green Area Ratio Measured through a Portable Monitoring System: With Particular Focus on the Case of Dalseo-Gu, Daegu Metropolitan City

This study analyzed the relationship between the measured concentration of particulate matter (PM) and green area, which is an important spatial factor affecting urban PM concentration and even more carbon neutral in cities. In order to investigate the effects of green area, the green area ratio (GAR) is used and correlated with particulate matter. As a result of analyzing the correlation between GAR and PM concentration using the measured PM data, it was found that there was a positive correlation between the two variables. Hence, the higher the GAR, the lower the PM concentration. It can be said that the introduction of spatial elements with high GAR scores, such as parks and forests in cities, is effective in reducing PM concentration. In this study, a portable PM monitoring system using a vehicle for PM measurement was also established and operated. As a result, it was found that a PM monitoring system using a light-scattering sensor is an effective PM measurement method that can be used at the local government level. It was also found that a follow-up study is needed in the future to identify the PM mitigation functions of urban green areas according to the detailed characteristics of green areas as well as various environmental factors. This study can be used in air quality improvement activities and efforts as reference data by policy decision makers and in the field of environmental planning associated with the removal of airborne particulate matter pollution in urban areas.

Optimization of Induction Charging of Water Droplets to Develop an Electrostatic Spray Scrubber Intended for Poultry Particulate Matter Mitigation

HighlightsInduction charging of water droplets by an ESS showed greatest CMRs using an 80° tapered edge flat spray nozzleCMR of 0.578 mC kg-1 achieved with a water mass flow rate of 0.397 kg min-1 and an applied voltage of -11.6 kVChanges in water conductivity did not significantly affect measured droplet CMRsAbstract. Particulate matter (PM) is a prominent air pollutant in poultry facilities. One promising technology for PM mitigation in poultry facilities is the electrostatic spray scrubber (ESS). However, there is a significant lack of information on the charging characteristics of an ESS to promote efficient droplet charging and poultry PM collection. This study quantified the effects of applied voltage, water mass flow rate, water conductivity, and spray pattern on the charge-to-mass ratio (CMR) of droplets charged by a ring-shape electrode during induction. Voltages from -5 to -14 kV were applied to a 100 mm diameter, cylindrical copper electrode ring to charge droplets sprayed from different nozzles using 80° hollow cone, 80° tapered edge flat, and 110° tapered edge flat spray patterns. It was found that CMRs increased with greater negative voltages up to a point, after which greater negative voltages would decrease the CMR. A similar trend was observed for the effects of water mass flow rate on CMR. Water conductivity showed inconsistent effects on CMR across spray patterns and was deemed a statistically significant but practically insignificant factor. The 80° tapered edge flat spray pattern was found to produce the greatest CMRs. A linear regression model (R2 = 0.865) was developed and optimized to find the ESS configuration resulting in the greatest droplet CMR. The optimal CMR was 0.578 mC kg-1, produced using an 80° tapered edge flat spray nozzle at a water mass flow rate of 0.397 kg min-1, a conductivity of 0.05 S m-1, and an applied voltage of -11.6 kV. Keywords: Droplet charging, Electrostatic scrubber, Electrostatic sprayer, ESS, Induction charging, Spray charging, Spray nozzle.

Particulate Matter (PM) Adsorption and Leaf Characteristics of Ornamental Sweet Potato (Ipomoea batatas L.) Cultivars and Two Common Indoor Plants (Hedera helix L. and Epipremnum aureum Lindl. &amp; Andre)

Particulate matter (PM) is a serious threat to human health, climate, and ecosystems. Furthermore, owing to the combined influence of indoor and outdoor particles, indoor PM can pose a greater threat than urban PM. Plants can help to reduce PM pollution by acting as biofilters. Plants with different leaf characteristics have varying capacities to capture PM. However, the PM mitigation effects of plants and their primary factors are unclear. In this study, we investigated the PM adsorption and leaf characteristics of five ornamental sweet potato (Ipomea batatas L.) cultivars and two common indoor plants (Hedera helix L. and Epipremnum aureum Lindl. &amp; Andre) exposed to approximately 300 μg m−3 of fly ash particles to assess the factors influencing PM adsorption on leaves and to understand the effects of PM pollution on the leaf characteristics of plants. We analyzed the correlation between PM adsorption and photosynthetic rate (Pn), stomatal conductance (gs), transpiration rate (Tr), leaf area (LA), leaf width/length ratio (W/L), stomatal density (SD), and stomatal pore size (SP). A Pearson’s correlation analysis and a principal component analysis (PCA) were used to evaluate the effects of different leaf characteristics on PM adsorption. The analysis indicated that leaf gas exchange factors, such as Pn and Tr, and morphological factors, such as W/L and LA, were the primary parameters influencing PM adsorption in all cultivars and species tested. Pn, Tr, and W/L showed a positive correlation with PM accumulation, whereas LA was negatively correlated.

Mitigation of Particulate Matter and Airborne Pathogens in Swine Barn Emissions with Filtration and UV-A Photocatalysis

This study evaluated the use of filtration and UV-A photocatalysis for the reduction of particulate matter (PM) and airborne bacterial pathogens in swine barns. Two MERV filters (8 and 15) were used to mitigate PM concentrations measured at the PM 1, PM 2.5, respirable PM, and PM 10 ranges. Filtration was also used to generate different levels of airborne pathogens to be treated by UV-A. Results show that MERV 8 and 15 filters effectively reduced PM concentrations (96–98%) in air exhausted from a swine barn (p ranged from &lt;0.01 to 0.04). UV-A photocatalysis did not mitigate PM concentrations. UV-A photocatalysis treatment reduced measured colony-forming units (CFUs) by 15–95%. The CFU percent reduction was higher when airborne PM concentration was low. The numeric results suggested a real mitigation effect despite p-values that did not meet the usual statistical cut-off of &lt;0.05 for significance due to the large variability of the CFU control samples. Normalization of measured airborne pathogen concentrations by smaller PM size range concentrations led to emerging significant treatment differences for CFUs. A significant decrease (~51% reduction; p &lt; 0.02) in the concentration of viable airborne bacteria was shown for all PM below the 10 micron range.

Modelling and optimisation of a wire-plate ESP for mitigation of poultry PM emission using COMSOL

Particulate matter (PM) emissions constitute a major air pollution concern for commercial poultry production facilities. Recently, the electrostatic precipitator (ESP) has emerged as a promising technology for mitigation of poultry PM. However, further optimisation of ESP design, operation scale, and operating parameters is necessary for improved PM removal efficiencies and practical applications in poultry facilities. In this study, a comprehensive model of the poultry PM collection process in a simulated ESP module was developed using COMSOL software. The model simulated airflow characteristics, electric field and space charge development, particle charging, and particle trajectory tracking of both PM10 and PM2.5. Wire-plate ESP configurations with varying design parameters, such as number of wire electrodes and plate separation distance, were simulated using the COMSOL model at an inlet air velocity of 2.0 m s−1 to identify the optimal configuration for development of ESP physical prototypes. The model was validated by laboratory testing of a prototype ESP within a wind tunnel under three sets of ventilation conditions representing hot, warm, and cold weather, respectively. The prototype ESP exhibited PM10 collection efficiencies of 90.8%, 97.1%, and 99.0% in hot, warm, and cold weather conditions, respectively. The observed PM2.5 collection efficiencies under these respective conditions were 86.9%, 94.4%, and 97.8%. This COMSOL model can be used as an inexpensive alternative to developing physical prototypes when evaluating ESP designs for PM mitigation in poultry facilities.

Net particulate matter removal ability and efficiency of ten plant species in Beijing

Particulate matter (PM) removal is a major component of ecosystem services provided by urban green infrastructure. The annual PM removal service of plant species has generally been determined based on net PM removal ability. However, little is known about the leaf surface area normalized net PM mass removal abilities and efficiencies of different species. Net PM removal ability is the PM removed from the atmosphere by leaf surface during a period. We evaluated the net PM removal abilities and efficiencies of ten species based on quantitative leaf surface PM 1 day after and 21 days after a rainfall event. Net PM removal ability differed between plant species during the study period, and the net PM removal abilities were strongly correlated with the leaf surface PM 21 days after the rainfall, and this relationship weakened with the decrease of PM diameter. Plant species had significantly different net PM removal efficiencies except for large PM, and net PM removal efficiency was lower for coniferous trees and higher for species with high self-cleaning ability during our study period. Species differed in net PM removal ability and efficiency, and there were greater differences in the net removal ability of fine and coarse PM compared with large PM; therefore, care should be taken when species are selected for fine and coarse PM mitigation.

Mitigation of Odor and Gaseous Emissions from Swine Barn with UV-A and UV-C Photocatalysis

UV-A (ca. 365 nm wavelength, a.k.a. ‘black light’) photocatalysis has been investigated to comprehensively mitigate odor and selected air pollutants in the livestock environment. This study was conducted to confirm the performance of UV-A photocatalysis on the swine farm. The objectives of this research were to (1) scale-up of the UV-A photocatalysis treatment, (2) evaluate the mitigation of odorous gases from swine slurry pit, (3) test different UV sources, (4) evaluate the effect of particulate matter (PM) and (5) conduct preliminary economic analyses. We tested UV-A photocatalysis at a mobile laboratory-scale capable of treating ~0.2–0.8 m3·s−1 of barn exhaust air. The targeted gaseous emissions of barn exhaust air were significantly mitigated (p &lt; 0.05) up to 40% reduction of measured odor; 63%, 44%, 32%, 40%, 66% and 49% reduction of dimethyl disulfide, isobutyric acid, butanoic acid, p-cresol, indole and skatole, respectively; 40% reduction of H2S; 100% reduction of O3; and 13% reduction of N2O. The PM mitigation effect was not significant. Formaldehyde levels did not change, and a 21% generation of CO2 was observed. The percent reduction of targeted gases decreased as the airborne PM increased. Simultaneous chemical and sensory analysis confirmed that UV-A treatment changed the overall nuisance odor character of swine barn emissions into weaker manure odor with ‘toothpaste and ‘mint’ notes. The smell of benzoic acid generated in UV-A treatment was likely one of the compounds responsible for the less-offensive overall odor character of the UV-treated emissions. Results are needed to inform the design of a farm-scale trial, where the interior barn walls can be treated with the photocatalyst.

Elemental Characteristics of Respirable Particulate Matter in the Urban Atmosphere of Dehradun, Uttrakhand, India

To evaluate the ambient air quality of the Dehradun city, respirable particulate matter was collected using respirable dust sampler (RDS) and analysed for the heavy metal content using atomic absorption spectroscopy (AAS). The morphology of particulates were determined using scanning electron microscope (SEM) and the elemental composition was determined through SEM- energy dispersive spectroscopy (EDS). Particulate matter mass concentration ranged from 65.00 µg m-3 to 337.33 µg m-3. Quantified heavy metals in particulate matter were Copper (Cu), Zinc (Zn), Cobalt (Co), Manganese (Mn), Iron (Fe), Nickel (Ni), Chromium (Cr), Lead (Pb) and Cadmium (Cd). The order of concentration of heavy metals were found to be in the trend of Fe&gt;Zn&gt;Cu&gt;Pb&gt;Cr&gt;Ni&gt;Mn&gt;Co&gt;Cd. Maximum concentration of PM10 was found at commercial site during summer, winter and monsoon season. Enrichment factor analysis showed substantial contribution of anthropogenic activities on PM10. Source apportionment (varimax rotated factor analysis method) showed dominance of incineration and uncontrolled burning of waste and refuses, resuspended dust with vehicular emission and crustal sources as the dominant sources in Dehradun. Plantation drive strategy have major role in ambient particulate matter mitigation measures and carbon sequestration from climate change and global problem worldwide. This study will be help to mitigate or decrease the load of air pollution by the using of various trees for sustainable human development on the marvellous earth planet.

Performance characteristics of a fan filter unit (FFU) in mitigating particulate matter levels in a naturally ventilated classroom during haze conditions.

The performance of a low-cost fan filter unit (FFU) in mitigating hazardous particulate matter (PM) levels in a naturally ventilated school classroom is presented. The FFU can be considered as a simplified mechanical ventilation and air-conditioning system without heating and cooling functions. The FFU improves indoor air quality through introduction of cleaned outdoor air to flush out internally generated heat and moisture and reducing infiltration by maintaining indoor pressurization. Indoor particle number concentrations were reduced between 85% and 95%. The particle removal performance (PRFFFU ) of the FFU is determined and incorporated into the augmented façade penetration factor (Paug ). A case-specific recursive dynamic mass balance model is used to characterize the infiltration factor (FINF ), deposition rate (K), and the penetration efficiency (Paug ) from continuously monitored indoor and outdoor mass concentration levels. Computed "Paug " (0.07, 0.09, and 0.13) and "FINF " (0.06, 0.08, and 0.11), respectively, for PM10, PM2.5, and PM1 suggest that exposure to PM was significantly reduced indoors. The effectiveness of the FFU for reduced "FINF " and "Paug " may be attributed to its superior filtration, dilution, and exfiltration mechanisms. In comparison with alternative PM mitigation solutions, the FFU is effective, affordable, and sustainable.

Relationship between Remotely Sensed Ambient PM10 and PM2.5 and Urban Forest in Seoul, South Korea

Currently particulate matter (PM) is one of the major threats to public health and safety in urban areas such as Seoul, South Korea. The limited amount of air-quality monitoring systems may not provide sufficient data or coverage, in particular on the spots of urban forest. Considering urban forest as a possible contributor to mitigate PM in an urban area, this study investigated the relationship between the size and topography of urban forests near the air-quality monitoring stations and PM measurements from those stations. The average of PM measurements during the study period of August 2017 to July 2019 was computed into three different domains by using three concentric buffers from 25 monitoring stations distributed across Seoul. To estimate PM concentrations, multiple linear regression models were developed by using satellite-borne multi-spectral band data retrieved from Moderate Resolution Imaging Spectroradiometer onboard Terra (MODIS) and Landsat 8 in conjunction with meteorological data sets. Overall, PM10 and PM2.5 measurements significantly varied with season and tended to be lower with large urban forests than small ones by 5.3% for PM10 and 4.8% for PM2.5. Overall, PM10 and PM2.5 measurements were lower at the domains encompassing high urban forests in elevation than those of relatively flattened forests by 9.1% for PM10 and 3.9% for PM2.5. According to the findings from this study, the topographical difference among urban forests could exert a more significant influence on PM mitigation. The result from correlation analysis between the PM estimates from Landsat 8-based models and ground-based PM measurements was considered reliable based on Pearson’s coefficients of 0.21 to 0.74 for PM10 and −0.33 to 0.74 for PM2.5. It was considered that using a satellite imagery-derived PM model could be effective to manage urban forest over a large area which in general implies the limitation of data collection.