Particulate Matter Monitoring Research Articles

Comprehending particulate matter dynamics in transit-oriented developments: Traffic as a generator and design as a captivator

In Transit-Oriented Development (TOD), the close integration of residential structures with community activities and traffic heightens residents' exposure to traffic-related pollutants. Despite traffic being a primary source of particulate matter (PM), the compact design of TODs, together with the impact of urban heat island (UHI), increases the likelihood of trapping emitted PM from traffic, leading to heightened exposure of TOD residents to PM. Although PM originates from two distinct sources in road traffic, exhaust and non-exhaust emissions (NEE), current legislation addressing traffic-related PM from non-exhaust emissions sources remains limited. This paper focuses on two TOD typologies in Manchester City—Manchester Piccadilly and East Didsbury—to understand the roles of TOD traffic as a PM generator and TOD place design as a PM container and trapper. The investigation aims to establish correlations between street design canyon ratios, vehicular Speed, and PM10/PM2.5, providing design guidance and effective traffic management strategies to control PM emissions within TODs. Through mapping the canyon ratio and utilising the Breezometer API for PM monitoring, the paper revealed elevated PM levels in both TOD areas, exceeding World Health Organization (WHO) recommendations, particularly for PM2.5. Correlation analysis between canyon configuration and PM2.5/PM10 highlighted the importance of considering building heights and avoiding the creation of deep canyons in TOD design to minimise the limited dispersion of PM. Leveraging UK road statistics and the PTV Group API for vehicle speed calculations, the paper studied the average speeds on the TOD roads concerning PM. Contrary to conventional assumption, the correlation analyses have revealed a noteworthy association shift between vehicular speed and PM concentrations. A positive correlation existed between speed increase and PM increases on arterial roads. However, a negative correlation emerged on main, collector, and local streets, indicating that PM levels rise for both PM10 and PM2.5 as Speed decreases. These findings challenge the traditional assumption that higher Speed leads to increased emissions, highlighting the potential impact of NEE on PM concentrations. This paper calls for thorough design considerations and traffic management strategies in TOD, especially in dense areas, considering building height, optimising traffic flow, and enhancing compromised air quality associated with vehicular emissions.

Particulate matter (pm10) monitoring in the United Arab Emirates using a satellite remote sensing based model

Particulate matter (PM) is one of the major factors causing air pollution, which is considered a concern for human health. Hence, measuring and monitoring the concentrations of these particles is essential. In this study, the main objective is to develop a remote sensing based PM10 monitoring model for the United Arab Emirates (UAE) using Landsat 8 imagery. Landsat 8 images acquired during the four-year period from 2016 to 2022 were obtained and used along with PM10 data collected at 41 ground monitoring stations corresponding to the acquisition of the satellite data (data from 30 stations used for model development 11 stations were used for model testing). The Landsat 8 data was obtained from the United States Geological Survey (USGS) Core Science Systems in the form of Digital Numbers (DNs). The DNs of the four optical bands of Landsat 8 were then converted to top of the atmosphere reflectance (TOA) through radiometric processing, and then used to estimate the Aerosol Optical Thickness. A spectral PM10 model was then developed through regression analysis, correlating AOT to PM10 values obtained at the ground stations. The model provided an R-squared value of 65% and a Root Mean Square Error (RMSE) of 12.55 µg/m3. The results suggest that the developed model is robust in estimating PM10 values and can therefore be used for satellite-based monitoring at any location in the UAE.

Analysis of measurement uncertainty for verification/calibration of particulate matter measuring instruments using the gravimetric method

As demand for particulate matter (PM) monitoring increases for scientific and regulatory purposes, the need for an accurate calibration process for these instruments will also increase. This article focuses on studying the factors that cause measurement uncertainty during the calibration process with the calibration facility (ManDust) for reference gravimetric instruments. Measurement uncertainty in this article included type A and type B uncertainties. Type A uncertainty mostly included repeatability and reproducibility while volume uncertainty predominately contributed to type B uncertainty. To clearly examine how these factors contributed to the combined uncertainty, the calibration process was carried out at a high concentration range from 0.4 to 1.6 mg m−3. In general, the measurement uncertainty ranged from 1.95% to 6.65%. Further analysis showed that type A uncertainty contributed more to the total uncertainty than type B. In particular, uncertainty from volume calculation and measurement contributed over 80% of type B uncertainty. During the PM filter weighing process, the uncertainty during the conditioning process contributed the most to type B uncertainty. This article also conducted a sensitivity analysis and found that besides volume measurement, the filter weighing procedure was also sensitive to changes even though it only contributed less than 5% of type B uncertainty. Using high-quality devices and careful handling during these two procedures should be emphasized to reduce the uncertainty of the calibration process.

Particulate Matter Pollution and Health Impacts in Six North African Countries

This paper addresses the issue of outdoor particulate matter pollution in six North African countries-Algeria, Egypt, Libya, Morocco, Sudan, and Tunisia. Major particulate matter sources include various industries and transportation systems, dust storms, and sandstorms. It has been shown that there is a need to increase public awareness and participation and enhance capacities regarding ground-based monitoring of particulate matter, using satellite data, and assessing the health impacts of exposure to particulate matter. There is also a need for improved legislation and better governance regarding its implementation and enforcement. If possible, examples of potential impacts of particulate matter pollution on the Gross National Product (GNP) for various economic and health sectors will be reported. As outdoor particulate matter pollution is one of the major sources of loss to the GNP, air quality management needs to be strengthened.

Evaluation of the effects of dust pollution on specific plant species near and around the marble mining site in Rajasthan, India.

Airborne particles (dust pollution) pose a significant threat to both human and plant populations. Plant leaves act as crucial biofilters, capturing significant amounts of air pollution; this characteristic offers a valuable tool to measure local pollution levels and assess individual plant species' ability to intercept and mitigate harmful dust particles. The present study was carried out to asses the effect of responses of various plant species to dust pollution near and around the marble mining site comprising residential site, highway area, and Central University of Rajasthan as control. The anticipated pollution index, air pollution tolerance index (APTI), dust absorption capacity, metal accumulation index (MAI), and biochemical factors were used to evaluate plant responses. Azadirachta indica A. Juss. demonstrated the highest (29.0) and Vachellia nilotica L. showed lowest (5.6) APTI, respectively. A. indica showed maximum MAI values in comparison to other plant species situated at residential site. Additionally, monitoring of particulate matter (PM10) observed to highest at highway, followed by mining, residential, and control sites. Overall A. indica representing highest APTI and effective dust capturing capacity at all sites could serve as potential pollution sinks. V. nilotica, with its very low APTI, can be marked as biomonitoring tool for detecting dust pollution.

Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa

Recent advances in low-cost (LC) sensor technology fostered their deployment in low-income and undersampled countries such as Sub-Saharan Africa (SSA) regions, affected by the highest particulate matter (PM) concentrations and population exposure. The present study is the first addressed in Niamey, Niger, and focuses on assessing LC sensor data and global reanalysis products. Three LC PM2.5 and PM10 monitoring stations were deployed and successfully operated across ∼8 months at different (urban, suburban and rural) locations.Observed PM2.5 and PM10 concentrations revealed consistent patterns, higher during the dry Harmattan season, while appreciably lower during the humid monsoon season. In Niamey, PM2.5 mean concentrations (6.1–20.1 μg/m3) were similar to those observed over higher-income countries, confirming the hypothesis of strictly depending on urbanisation, and thus on anthropogenic activities. Conversely, PM10 concentrations (55.3–142.8 μg/m3) were remarkably higher than most of those measured elsewhere worldwide, and predominantly constituted (81–89%) by coarse fraction. PM10 origin, inferred by backtrajectory analysis, was mainly natural (Saharan dust) during the Harmattan season, and both natural and anthropogenic during the monsoon season.Low-resolution gridded estimations by the Copernicus Atmosphere Monitoring Service (CAMS) were not capable of adequately resolving the spatial variability of PM2.5 and PM10 observations in Niamey, further highlighting the importance of sensor network data to improve air quality knowledge. To tackle the harmful effects of Saharan dust on population, and create robust datasets integrated with gridded products in this challenging region, effort should be put toward creation of trans-national integrated monitoring networks based on LC sensors across SSA.

Personal exposure monitoring of fine and coarse particulate matter using exposure assessment models for elderly residents in Hong Kong

This study investigated the determinants of personal exposures (PE) to coarse (PM2.5-10) and fine particulate matter (PM2.5) for elderly communities in Hong Kong. The mean PE PM2.5 and PM2.5-10 were 23.6 ± 10.8 and 13.5 ± 22.1 μg/m3, respectively during the sampling period. Approximately 76% of study subjects presented statistically significant differences between PE and ambient origin for PM2.5 compared to approximately 56% for PM2.5-10, possibly due to the coarse-size particles being more influenced by similar sources (road dust and construction dust emissions) compared to the PM2.5 particles. Individual PE to ambient (P/A) ratios for PM2.5 all exceeded unity (≥1), suggesting the dominant influences of non-ambient particles contributed towards total PE values. There were about 80% individual P/A ratios (≤1) for PM2.5-10, implying possible effective infiltration prevention of larger size particulate matter particles leading to dominant influences from the outdoor sources. The higher concentration of NO3− and SO42− in PM2.5-10 compared to PM2.5 suggests possible heterogeneous reactions of alkaline minerals leading to the formation of NO3− and SO42− in PM2.5-10 particles. The PE and ambient OC/EC ratios in PM2.5 (8.8 ± 3.3 and 10.4 ± 22.4, respectively) and in PM2.5-10 (6.0 ± 1.9 and 3.0 ± 1.1, respectively) suggest possible secondary formed OC from surrounding rural areas. Heterogeneous distributions (COD >0.2) between the PE and ambient concentrations were found for both the PM2.5 and PM2.5-10 samples. The calibration coefficient as the association between personal and surrogate exposure measure of PE to PM2.5 (0.84) was higher than PM2.5-10 (0.52). The findings further confirm that local sources were the dominant contributor to the coarse particles and these coefficients can potentially be used to estimate different PE to PM2.5 and PM2.5-10 conditions. A comprehensive understanding of the PE to determinants in coarse particles is essential to further reduce potential exposure misclassification.

Design and Evaluation of a Dryer System for IoT Hyperlocal Particulate Matter Monitoring Device

Design and Evaluation of a Dryer System for IoT Hyperlocal Particulate Matter Monitoring Device

An In-Field Assessment of the P.ALP Device in Four Different Real Working Conditions: A Performance Evaluation in Particulate Matter Monitoring.

This study aimed to assess the performance, in terms of precision and accuracy, of a prototype (called "P.ALP"-Ph.D. Air Quality Low-cost Project) developed for monitoring PM2.5 concentration levels. Four prototypes were co-located with reference instrumentation in four different microenvironments simulating real-world and working conditions, namely (i) office, (ii) home, (iii) outdoor, and (iv) occupational environments. The devices were evaluated for a total of 20 monitoring days (approximately 168 h) under a wide range of PM2.5 concentrations. The performances of the prototypes (based on the light-scattering working principle) were tested through different statistical methods. After the data acquisition and data cleaning processes, a linear regression analysis was performed to assess the precision (by comparing all possible pairs of devices) and the accuracy (by comparing the prototypes against the reference instrumentation) of the P.ALP. Moreover, the United States Environmental Protection Agency (US EPA) criteria were applied to assess the possible usage of this instrumentation, and to evaluate the eventual error trends of the P.ALP in the data storage process, Bland-Altman plots were also adopted. The outcomes of this study underlined that the P.ALP performed differently depending on the microenvironment in which it was tested and, consequently, on the PM2.5 concentrations. The device can monitor PM2.5 variations with acceptable results, but the performance cannot be considered satisfactory at extremely low and remarkably high PM2.5 concentrations. Thanks to modular components and open-source software, the tested device has the potential to be customized and adapted to better fit specific study design needs, but it must be implemented with ad hoc calibration factors depending on the application before being used in field.

Field calibration of low-cost particulate matter sensors using artificial neural networks and affine response correction

Due to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing imperfections. The objectives of this paper include (i) introduction of an innovative approach to field calibration for low-cost PM sensors using artificial intelligence methods, (ii) implementation of the calibration procedure involving optimized artificial neural network (ANN) and combined multiplicative and additive correction of the low-cost sensor readings, (iii) demonstrating the efficacy of the presented technique using a custom-designed portable PM monitoring platform and reference data acquired from public measurement stations. The results obtained through comprehensive experiments conducted using the aforementioned low-cost sensor and reference data demonstrate remarkable accuracy for the calibrated sensor, with correlation coefficients of 0.86 for PM1 and PM2.5, and 0.76 PM10 (particles categorized as having diameter equal to or less than 1 μm, 2.5 μm, and 10 μm, respectively), along with low RMSE values of only 3.1, 4.1, and 4.9 µg/m3.

Assessment of air pollution emitted during cooking using biomass and cleaner fuels in the Shiselweni region of Eswatini (Swaziland)

AbstractIn Eswatini, 62.3% of households still rely on solid fuel for cooking, especially wood (61.8%). Smoke emissions during biomass burning remain the primary source of indoor air pollution, comprising pollutants detrimental to health. This study provides a quantitative exposure assessment of fuels used in the Shiselweni region with the objectives of monitoring the carbon monoxide (CO) and carbon dioxide (CO2) concentrations during cooking events and evaluating cancer and noncancer risks due to exposure to particulate-bound polycyclic aromatic hydrocarbons (PAHs) during cooking events among cooking personnel in households that cook indoors by burning biomass. Real-time CO, CO2, and particulate matter (PM) monitoring was performed in seventeen kitchens during cooking events across the Shiselweni region using different cooking methods: biomass in open fires and stoves, liquefied petroleum gas (LPG), and electric stoves. PM2.5, PM10, and CO exceeded indoor exposure guidelines for biomass fuel-related homesteads. Particulate PAH intake concentrations were evaluated, and biomass fuel users exhibited high cancer risks and low embryo survival chances due to particulate PAH exposure. The average total cost of reducing the incremental lifetime cancer risk (ILCR) and hazard quotient (HQ) to acceptable levels was lower when shifting from biomass to LPG stoves than when shifting to electric stoves. Graphical abstract

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM2.5), a significant portion of ambient PM2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM2.5, are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM2.5 both temporally and spatially. Monthly network-average BC was 3 × higher in winter than late spring, while PM2.5 was only 10% lower. In both seasons, average PM2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM2.5, and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM2.5. Thus, including BC in addition to PM2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

Ambient particulate matter monitoring using bright field imaging-based sensor

The bright-field imaging of the particulate matter is demonstrated with the customized bright-field (BF) microscopic unit. Polystyrene spheres of size 4.5 µm as reference objects have been imaged to validate the claim. An image-processing-based algorithm has been used which uses pixel size for particle sizing and distribution. The lateral resolution of the device is found to be approximately 0.5 µm and the field of view is 56 µm × 42 µm which has been utilized for the imaging of the ambient PM. The device gives the results in the form of particulate matter concentration, size distribution, and shape of particulate matter.

Development of end-to-end low-cost IoT system for densely deployed PM monitoring network: an Indian case study

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

Direct Measurement of Aerosol Liquid Water Content: A Case Study in Summer in Nanjing, China.

Aerosol liquid water content (ALWC) affects the mass loading, optical properties, and toxicity of aerosols. However, the measurement of ALWC is very rare due to its requirement of sophisticated instruments and its high operational costs. In this work, we improved on our previous simple, low-cost method by using a combination of one real-time fine particulate matter (PM2.5) monitor and two turbidimeters and successfully applied these for the direct measurement of ALWC in PM2.5 in Nanjing during the summer of 2023. The average ALWC during this measurement period occupied ~1/6 of the total PM2.5 mass, and this contribution was even greater with the elevation in the PM2.5 concentration. The ALWC was, as anticipated, closely related to the relative humidity (RH) and PM2.5 concentrations, but it did not always increase with the air quality index (AQI) due to the fact that polluted periods in summer were often governed by high O3 levels, not PM2.5 levels. The ALWC also had a great impact on visibility; it could decrease the visibility rapidly to hazy conditions when the dry PM2.5 was not high (~30 μg m-3) or the AQI was "good" (75~100), indicating that the air quality classified as "good" using the dry PM2.5 concentration might actually be "lightly polluted" if the ALWC is included. We also found that the air mass originating from Northeast China had the lowest PM2.5 mass concentration yet the highest ALWC values due to its high RH. Moreover, the quantification of ALWC levels can help us understand the solubility/bioavailability and thus the toxic effects of some specific components (for example, heavy metals or organics). Moreover, the influence of ALWC on air quality classifications should also be considered in the assessment of the health effects of air pollution and in public health early warning and protection.

Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine

Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

Concentrated fireworks display-induced changes in aerosol vertical characteristics and atmospheric pollutant emissions

Fireworks affect the atmospheric environment and human health due to the rapid release of high levels of air pollutants. It is crucial to clarify the vertical characteristics of air pollutants and quantify the emission quantity for accurate control and health assessment. With the use of an unmanned aerial vehicle and a ground-based LiDAR system, we investigated the vertical profiles of particulate matter (PM) under the policy of concentrated fireworks detonation in a megacity (i.e., Changchun) in Northeast China and developed emission factors for fireworks displays. The results showed that the PM2.5, PM10, SO2 concentrations within a height of 200 m in the core area of fireworks display significantly increased by 143.8 %, 63.7 % and 174.3 %, respectively, on the day of the Lantern Festival relative to the background concentrations. There was a decreasing trend in the PM2.5 concentration with increasing height, with a maximum vertical decrease of 64 μg/m3 per 100 m. Despite a general decline in the concentration, the vertical distribution of polluted air masses exhibited heterogeneity, occasionally exhibiting a bimodal distribution pattern. Moreover, the correlation coefficients between the aerosol optical depth (AOD) at different altitudes (0.2–0.4, 0.4–0.7, and 0.7–1.0 km) and the ground-level PM concentration were 0.29, −0.35, and −0.35, respectively. This phenomenon indicates a reduced influence of ground-level PM emission on the AOD at altitudes from 0.4 to 1.0 km and the increased potential for aerosol particles to escape upwards, particularly in the troposphere. Furthermore, an approach combining real-time stereoscopic monitoring of PM and downwind wind speed was developed to calculate the estimated firework emission factors. The emission factors recorded during the Lantern Festival, specifically between 20:00 and 24:00, were 128.6 ± 28.3 mg/m2 per 10 min and 92.7 ± 23.2 mg/m2 per 10 min for PM2.5 and PM10, respectively, including an uncertainty of 9 %–14 % generated by the sensors. The proposed approach and corresponding emission factors could be used to easily quantify air pollutant emissions originating from fireworks displays and to conduct further numerical simulations for air pollution regulation and health assessment.

Fine particle pollution during megafires contains potentially toxic elements

Wildfires that raged across Australia during the 2019–2020 ‘Black Summer’ produced an enormous quantity of particulate matter (PM) pollution, with plumes that cloaked many urban centres and ecosystems along the eastern seaboard. This has motivated a need to understand the magnitude and nature of PM exposure, so that its impact on both built and natural environments can be more accurately assessed. Here we present the potentially toxic fingerprint of PM captured by building heating, ventilation, and air conditioning filters in Sydney, Australia during the peak of the Wildfires, and from ambient urban emissions one year later (Reference period). Atmospheric PM and meteorological monitoring data were also assessed to determine the magnitude and source of high PM exposure. The wildfires were a major source of PM pollution in Sydney, exceeding the national standards on 19 % of days between November–February. Wildfire particles were finer and more spherical compared to Reference PM, with count median diameters of 892.1 ± 23.1 versus 1484.8 ± 96.7 nm (mean ± standard error). On an equal-mass basis, differences in potentially toxic elements were predominantly due to higher SO42−-S (median 20.4 vs 4.7 mg g−1) and NO3−-N (2.4 vs 1.2 mg g−1) in Wildfire PM, and higher PO43−-P (10.4 vs 1.4 mg g−1) in Reference PM. Concentrations of remaining elements were similar or lower than Reference PM, except for enrichments to F−, Cl−, dissolved Mn, and particulate Mn, Co and Sb. Fractional solubilities of trace elements were similar or lower than Reference PM, except for enhanced Hg (12.1 vs 1.0 %) and greater variability in Cd, Hg and Mn solubility, which displayed upper quartiles exceeding that of Reference PM. These findings contribute to our understanding of human and ecosystem exposures to the toxic components of mixed smoke plumes, especially in regions downwind of the source.

Spatiotemporal variations in emission and particulate matter concentration outside a concentrated layer feeding operation

A year-round monitoring of Particulate Matter (PM) less than 2.5 μm in aerodynamic diameter (PM2.5) and Total Suspended Particulate (TSP) from a conventional cage laying hen house was conducted to reveal their spatiotemporal variations of concentrations downwind of tunnel fans. The PM Emission Rate (ER) and the correlations between PM with meteorological parameters and ventilation rate were studied. Ten self-developed Particle Concentration Monitoring Units were distributed to multiple horizontal (2, 25, and 50 m) and vertical (2 and 5 m) locations for PM examination. Four PM2.5 concentration categories were classified: good (0–35 μg m−3), moderate (35–75 μg m−3), unhealthy (75–150 μg m−3), and very unhealthy (150–350 μg m−3). Annual and monthly percentages of PM2.5 and TSP sampling hours for each category were calculated. Significant temporal variations in ER were observed, with monthly average ER ranging from 0.05 to 0.86 mg d−1 hen−1 for PM2.5 and 1.40–8.90 mg d−1 hen−1 for TSP. Approximately 10–40% of PM2.5 sampling hours exceeded 75 μg m−3, while no TSP concentration was higher than the recommended threshold. The monthly percentage for the “very unhealthy” PM2.5 category was higher in winter, whereas the highest monthly percentage for the 800–1,200 μg m−3 TSP category was found in summer. Higher PM2.5 concentrations were prone to be observed at further sampling points, while higher TSP concentrations were mostly detected near the fans or on both sides of the sampling area. PM2.5 and TSP were positively correlated with ambient temperature and ventilation rate, while negatively correlated with relative humidity.

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.