Particulate Matter Measurements Research Articles

Aquality32: A low-cost, open-source air quality monitoring device leveraging the ESP32 and google platform

We present AQuality32, an open-source air quality monitoring device designed to address the challenges faced by small research teams in accessing affordable and versatile air quality monitoring solutions. The device utilizes an ESP32 System on Module (SoM) and integrates sensors for CO2 (SDC30 from Sensirion) and particulate matter (HM3301 from Seeed) measurement, along with temperature, relative humidity, and extensive sensing possibilities. It can be powered by a battery and is suitable for both stationary and on-the-go measurements. The paper details the hardware description, building instructions, programming, calibration procedures, and data collection setup for AQuality32. Validation experiments assess communication stability, geolocation accuracy, and environmental monitoring capabilities. The results demonstrate the device’s reliability, affordability, and suitability for various applications in environmental sciences, public health, and indoor/outdoor air quality monitoring. The paper emphasizes the importance of robust solutions, openness, and easy documentation for widespread adoption and impact in air quality research and monitoring.

Visualization of particulate matters in air using lensless microscopy

Abstract. Air quality is crucial for health, yet monitoring airborne particulate matter (PM), particularly PM2.5, remains challenging due to pollution and widespread distribution. PM2.5, with diameters less than 2.5 micrometers, are known to have significant health implications. Traditional methods of PMs detection and analysis are often time-consuming or require complex optical systems. This study explores the use of lensless microscopy as an innovative and cost-effective alternative for the visualization and measurement of PMs. Utilizing computational imaging techniques, lensless microscopy captures high-resolution images without the need for traditional lenses, facilitating rapid and accurate analysis of air quality. We describe the implementation of lensless imaging to detect and quantify PM2.5 particles in the air, highlighting the use of the single light wavelength and the computational reconstruction method. Our experiments demonstrated that this approach could effectively capture fine details of PMs, achieving resolutions comparable to traditional microscopy. This validates the potential of lensless microscopy as a practical tool for real-time air quality monitoring. By adopting lensless microscopy, we offer a more efficient and economical method for the continuous monitoring of PMs, which can significantly contribute to environmental health studies and pollution control efforts. According to specific needs, simply replacing the flow rate of the air pump can adapt to the PM measurement speed requirements in different environments. This research introduces a novel approach to air quality assessment, providing a promising solution for the rapid and detailed analysis of airborne particulate matter while at an affordable cost.

Comparing Airborne and Along-Sidewall Ground-Based Measurements to Retrieve Thermal and PM10 Concentration Profiles in Two Alpine Valleys

Abstract The paper aims at investigating the effectiveness of estimating vertical profiles of air temperature and PM10 concentrations in Alpine valleys through ground stations positioned at different altitudes on one valley sidewall (i.e., pseudovertical profiles). Two case studies in the Italian Alps are investigated: Chiese Valley in Trentino Province and Camonica Valley in the Lombardy region. Vertical profiles of temperature and PM10 concentrations were derived from airborne measurements at the center of the two valleys by means of low-cost sensors installed on a drone during summer 2019 and a tethered balloon during winter 2020. At the same time, five stations, equipped with the same kind of low-cost sensors, simultaneously monitored the same variables on one mountain slope. Comparisons between pseudoprofiles and airborne soundings revealed that ground stations well approximated temperature and PM10 soundings during the night and early morning, while temperatures along the slopes were higher than in the center of the valley during daytime, due to solar radiative heating, with larger differences in summer than in winter. On the contrary, some episodes with PM10 concentrations slightly higher in the valley center than on the slope were recorded, due to transport events and upslope winds. Nonetheless, the pseudoprofiles based on slope ground measurements faithfully reproduced the vertical gradients of both air temperature and PM10 if compared to those assessed from the soundings performed at the center of the two valleys. Results show that pseudovertical profiles can be a reliable and inexpensive method for continuous monitoring of vertical air temperature and PM10 distribution in mountain valleys. Significance Statement The purpose of this study is to understand whether ground-based measurements of air temperature and particulate matter on mountain slopes can be used as suitable surrogates of vertical atmospheric soundings with tethered balloons or drones in mountain environment. This is important because balloon and drone soundings are expensive and regulative constrained. The knowledge of the vertical distribution of air temperature is crucial to predict the distribution of air pollutants in valleys, namely, the particulate matter emitted by wood burning. Our results showed that ground-based measurements on mountain slopes made with low-cost sensors can satisfactorily reproduce vertical gradients of air temperature and distribution of particulate matter in a reliable and inexpensive way.

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%.

Assessment of vehicle exhaust PM emissions using high-resolution on-road measurements in Seoul, Korea

In megacities, road traffic is a major source of particulate matter (PM), requiring a critical understanding of effective air pollution control. Despite existing methods to determine PM emission factors (EFs) of vehicles, accurate estimation of PM emissions under real driving conditions remains challenging. We aimed to assess the EFs of organic aerosol (OA) and equivalent black carbon (eBC) from vehicles through on-road measurements in Seoul, Korea, to understand real-world PM emissions. We used a mobile laboratory equipped with an aerosol mass spectrometer and an aethalometer to measure the composition of PM. On-road measurements were conducted in vehicle tunnels, urban roadways, and residential areas, and the characteristics of measurement points were compared and analyzed. Our results showed that concentrations of OA increased proportionally with the influence of vehicle exhaust, while oxidation states of the OA decreased. Mobile measurements revealed spatial heterogeneities in aerosols, highlighting distinct characteristics of fresh OA on vehicle roads and elevated oxidation state values in residential areas. Active nitrate formation near vehicles led to elevated NO3 concentrations on roads compared to residential areas. Our study shows that mobile PM measurements, including OA and eBC, are valuable for the direct evaluation of emission inventories. However, given that the calculated EFs may not be applicable to other cities due to differences in vehicle composition and traffic conditions, the development of city-specific EFs will be necessary in the future. Furthermore, it is recommended to integrate this methodology with conventional emission inventories to identify vehicle-type-specific emissions.Graphical

Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring

During the past decade, substantial efforts have been dedicated to advancing cost-effective sensor platforms for air quality monitoring. Calibration is a crucial step in ensuring that the data quality of low-cost air monitoring systems meets established standards. Recent research has extensively evaluated low-cost air monitoring platforms against the data quality objectives set by the European Directive. This paper introduces a novel calibration model for low-cost air quality sensors, significantly improving the accuracy of the measurement of nitric oxide (NO), sulfur dioxide (SO2), and particulate matter (PM1, PM2.5, and PM10), while promoting accessibility and adaptability in environmental monitoring technologies. This study extends the evaluation of a developed platform capable of integrating a diverse array of sensors to measure up to 12 parameters. Our proposed models demonstrate a significant improvement, achieving a 60% better accuracy for SO2. Additionally, these models deliver similar results for PMx and NO or exceed those of state of the art research. The calibration methodology meets the requirements of the Data Quality Objectives (DQO) for all monitored parameters and also achieves indicative levels for PM parameters.

Evaluation of Current and Future Aviation Fuels at High-Pressure Rich-Quench-Lean-Type Combustor Conditions

Abstract Decarbonizing the aviation sector is one of the biggest challenges to minimize climate change effects associated with carbon dioxide emission. Sustainable aviation fuels will play a major role in this context especially for long-distance flights where hydrogen or all-electric propulsion systems do not present a feasible alternative. In addition, aromatic-free sustainable aviation fuels offer a promising solution to lower soot emissions of aeroengines. Jet A-1 as reference fuel, two blends, and two neat synthesized paraffinic kerosenes (SPKs) from hydroprocessed esters and fatty acids (HEFA) were tested to characterize the combustion behavior of drop-in/near drop-in fuels. Experiments in an optically accessible high-pressure rich-quench-lean (RQL)-type combustor at typical aeroengine conditions were performed using optical diagnostics, exhaust gas, and particle sampling measurements to delineate the fuel effects on spray, flame, and emission characteristics. Measurements were performed at pressures up to 10 bar, air preheating temperatures up to 773 K and primary zone equivalence ratios up to 1.66. Liquid fuel distribution and fuel placement were found to be sensitive to fuel properties. By contrast, no pronounced influence on flame position and shape were observed. As a consequence, NOx and CO emissions of the tested fuels differed only moderately. However, particulate matter measurements at the fuel richest operating condition showed a clear fuel ranking with the lowest particle emissions evident for the two neat HEFA-SPK samples. Moreover, the particle volume concentration at the fuel richest condition followed the expected trend with fuel H-content.

Terrestrial support of wetland food webs via a dissolved inorganic carbon pathway

AbstractStudies of terrestrial support of aquatic food webs have focused primarily on terrestrial organic matter (t‐OM) directly used by animal and microbial consumers. However, dissolved inorganic carbon (DIC) released from t‐OM might also support aquatic primary producers, a key resource for zooplankton and upper trophic levels. Using 2‐yr 13C measurements of algae, zooplankton, terrestrial detritus, sediments, dissolved and particulate organic matter from six seasonal wetlands, we found that in January when algal concentration was low, zooplankton used t‐OM directly or heterotrophic microbes that decompose t‐OM, but in March and May zooplankton was mainly supported by algae as their basal resources, and the algae used DIC derived from the mineralization and methanogenesis of t‐OM, suggesting the DIC pathway of terrestrial support of aquatic food webs. The decomposition of abundant t‐OM from both bed sediments and water column caused high concentrations of DIC. Despite uptake by algae, about half of DIC produced in January and March ended up as emissions to the atmosphere in the form of CO2. This finding revealed the dual roles of t‐OM in maintaining the productivity and stability of aquatic food webs and in contributing to global carbon emissions. This duality poses challenges for simultaneously mitigating carbon emissions and conserving biological communities in seasonal wetlands. Finally, wide seasonal differences in δ13C in DIC (−12.4‰ to 6.7‰) were observed, mainly driven by air–water CO2 exchange and photosynthesis, suggesting that 13C may be a powerful tool to investigate carbon cycling in shallow, temporary freshwater ecosystems that are widespread but understudied.

12513 Prenatal Traffic Exposures Are Associated With Earlier Age At Menarche

Abstract Disclosure: E.L. Silva: None. Z. Wang: None. S. Rifas-Shiman: None. A. Fleisch: None. H. Gibson: None. D. Gold: None. J. Chavarro: None. M. Hivert: None. E. Oken: None. B. Coull: None. J.E. Hart: None. T. James-Todd: None. S. Mahalingaiah: None. Background: The trend towards younger age at menarche over several decades has raised concern as earlier menarche is associated with various adverse health outcomes throughout adulthood including higher risk of early menopause, reproductive cancers, and metabolic disease. Some have suggested environmental exposures early in life may contribute to the temporal trend. Here we aimed to determine the extent to which prenatal residential measures of fine particulate matter (PM2.5), black carbon (BC), and other markers for traffic-related air pollution are associated with earlier age at menarche. Study Methods: Our study population included all female offspring in Project Viva, a pre-birth prospective cohort, with available age at menarche data. We used daily spatiotemporal models to estimate prenatal residential PM2.5 and BC exposures overall and by trimester. Residential proximity to major roadway and traffic density within a 100m buffer were measured at birth. Age at menarche was assessed via questionnaires during pre-teen and teen years. We used Cox proportional hazards modeling, adjusted for sociodemographic variables and census tract median household income, to estimate hazard ratios (HRs) for the association between air pollutants and age at menarche (months), where HR&amp;gt;1 indicates earlier onset of age at menarche associated with increased exposure to air pollutants. We weighted observations by the inverse probability of having available outcome data among all female offspring to account for cohort attrition. Results: The study included 638 females born between 2000 and 2003. Mean (standard deviation (SD)) age at menarche was 12.7 (1.4) years. Mean (SD) prenatal PM2.5 and BC exposures were 11.3 (1.5) g/m3 and 0.72 (0.21) g/m3, respectively. Only 7% of the cohort lived within 100m of a major roadway at birth. Prenatal PM2.5 exposure was not associated with age at menarche and the exposure distribution was mostly below US standards at the time (range: 6.8-15.8 g/m3, interquartile range (IQR): 2.0-3.0 g/m3). Higher overall prenatal and 3rd trimester BC were associated with earlier age at menarche [e.g., HR: 1.26 (95% CI, 1.04, 1.54) per IQR increment in 3rd trimester BC]. Living within 100m of a major roadway (versus 200m) [HR = 1.42 (95% CI, 1.08, 1.86)] and living in areas in the 2nd or 3rd (versus 1st) tertile of traffic density [2nd tertile: HR = 1.37 (95% CI, 1.07, 1.76); 3rd tertile: HR = 1.30 (95% CI, 1.02, 1.66)] at birth were also associated with earlier age at menarche. Conclusions: Higher prenatal exposure to BC, closer residential proximity to road major roadways, and higher traffic density were all associated with earlier age at menarche. The 3rd prenatal trimester and the window around birth may be especially important for traffic-related endocrine-disrupting and pro-inflammatory exposures with potential to accelerate puberty timing among females. Presentation: 6/1/2024

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57µg/m3 and PM10 at 39.58µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Impact of Design Dimension Optimization on Capacitive Sensor Performance for Particulate Matter Detection and Measurement

Particulate matter (PM) emissions from exhaust gases are major pollutants and cause serious health problems. Diesel particulate filters (DPF) are used for monitoring and trapping particulates from exhaust gases. For sensing these particulates sensors are used downstream of a DPF. The present study proposes an interdigitated electrode (IDE) capacitive sensor for detecting and measuring deposited particulates on the sensor surface. The sensor dimensions are optimized to detect and measure the least deposition of particulates. The paper presents improvement in sensor performance, a high selectivity of 65.70% with an accuracy of 87.26%. Dimension optimization extracts capacitance of 581 pF manifolds 10 times more than the reference sensor from the literature. The study presents a sensor with a thin sensing layer manufactured by optical lithography and a lift-off method for IDE. Measurements and testing showed that the sensor measures the lowest particulate mass of 0.0045 mg in 3.4 ms

Wind and river effects on a coastal current in Chatham Sound, British Columbia

Chatham Sound is an ecologically and economically important semi-enclosed basin in the northeast Pacific which has received only limited oceanographic attention. Here we investigate the surface circulation in the southern Sound by analyzing hourly current fields obtained from a high-frequency (HF) radar array, installed there in 2017 and still in operation, as well as other hydrographic and mooring measurements that allow us to characterize subsurface features at several specific locations during 2018 and 2019, and satellite imagery processed to obtain a measure of suspended particulate matter at high spatial resolution. We find that the surface circulation is dominated by the plume of the Skeena River, which flows into the southeast corner of the Sound. On average, this plume forms a northward flowing coastal current in the eastern half of the Sound, and is turbid during the freshet. The plume width changes only marginally even though the Skeena’s flow changes seasonally by a factor of about 7. However, the depth of the plume and its salinity is dramatically different during the early summer freshet compared to values in winter. Fastest plume currents appear near the offshore edges of the plume, even though the plume depth is greatest near the coast. We show that these features are consistent with those predicted from an analytical 1-1/2 layer buoyant coastal current model. Winds can accelerate or retard the coastal current; when strong enough the flow is arrested and turbid plume water fills the southern Sound.

Particulate matter in necropsy activities: experience from a health operators’ exposure monitoring campaign

BackgroundOperators in the obituary and necropsy sectors are exposed to various environmental hazards during specific tasks. Despite this exposure, occupational risks have often been underestimated, resulting in a lack of substantial evidence. The primary objectives of this study were to identify sources of chemical risk, establish procedures for monitoring and quantifying exposure during necropsy activities, and recommend adjustments to regulatory guidelines to protect the health of the operators. The study was conducted at the Legal Medicine Unit of the Umberto I General Hospital in Rome, focusing on the quantitative measurement of particulate matter (PM) exposure among at-risk operators during necropsy activities. Environmental levels of total suspended particles, PM10, PM4, PM2.5, and PM1 were assessed by evaluating the average, minimum, and maximum instantaneous indoor concentrations using an airborne analyzer.ResultsThe monitoring activities revealed that the PM concentrations were significantly lower than the recognized reference values. However, bone sawing, body removal, and cleaning were identified as high-risk maneuvers for dust suspension.ConclusionsOur study highlighted specific risks associated with necropsy activities, particularly concerning timing and certain maneuvers. These results may lead to interventions for improving current prevention procedures, implementing good practices, and developing specific guidelines to enhance operator safety.

The level, source, and health outcome of PM2.5 exposure in Southwest Iran

IntroductionDusty storms considerably increase airborne particles in dry and semi-dry locations, such as deserts with no plants and strong winds. Therefore, the environment and people are affected severely. Ahvaz, an important metropolis, is often polluted by neighboring nations. The present research studies the concentration, source, and calculation of these particles' effects. Material and methodFor health consequences evaluation, the WHO suggests the Air Quality Health Impact Evaluation Programmed (Air Q 2.2.3). Khuzestan Meteorology Office recorded particulate matter measurements on both hazy and clear days. The data was gathered voluminously in 2023. ResultAccording to data collected from Khuzestan province's meteorology documents, 49 days in 2023 had very unsafe air quality. The most polluted months in terms of the number of dust days are as follows: January (14 days) > December (12 days) > November (11 days) > August (5 days) > May (3 days) > September (2 days) > March, February, June, July, October (1 day) > April (0 days). HYSPLIT maps indicate that Iran causes dust in March, the Great Arabian Desert in December and August, Iraq in April, September, and October, Kuwait in January, Turkey in February and July, Egypt in June and May, and Oman in November. ConclusionThe meteorology database reveals that Ahvaz is highly polluted and that 49 days had unacceptable dust levels. Based on assessments obtained employing the Air Q+ programs, the people of Ahvaz encountered heart disease, respiratory disease, and stroke caused by their exposure to PM2.5 particulates.

First Results From a Calibrated Network of Low-Cost PM2.5 Monitors in Mombasa, Kenya Show Exceedance of Healthy Guidelines.

The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub-Saharan Africa. Well calibrated low-cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node-S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya's second largest city. As-reported Clarity Node-S data from January 2023 through April 2023 was moderately correlated with the TEOM-1400a measurements (R 2=0.61) and exhibited a mean absolute error (MAE) of 7.03μgm-3. Employing three calibration models, namely, multiple linear regression (MLR), Gaussian mixture regression and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40μgm-3 respectively. The R 2 value improved to 0.63 for the MLR model but all other models registered a decrease (R 2=0.44 and 0.60 respectively). Applying the correction factor to a five-sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5 within the city ranged from 12 to 18μgm-3. The concentrations exceeded the WHO daily PM2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

ABSTRACT A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system’s 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Airborne particulate matter measurement and prediction with machine learning techniques

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data

Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch's t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 μg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 μg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure—a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.

Particulate matter emissions from open pit mines; measurement methodologies, instruments, and research undertaken

Particulate matter is one of the primary pollutants in open pit mining operations. Measurements must be taken to control particulate matter created during open pit mining activities and to compare them to the regulatory limits. Numerous studies have been undertaken to estimate particulate matter emissions produced by open pit mining. It was discovered that the research were largely conducted on coal mines (69.4%), with little study done in other mining types. Research studies on particulate matter estimation took into consideration mostly machine characteristics (loader bucket volume, truck capacity, number of truck wheels etc.) and atmospheric conditions (air temperature, wind speed, relative humidity etc.). This study emphasizes on particulate matter measurement methods along with other measuring parameters and equipment for particulate matter estimation (TSP, PM10, PM4, PM2.5, and PM1).

Second-Hand Tobacco Smoke Exposure: Results of Particulate Matter (PM2.5) Measurements at Hospitality Venues in Addis Ababa, Ethiopia.

In Ethiopia, a comprehensive smoke-free law that bans smoking in all public areas has been implemented since 2019. This study aimed to evaluate compliance with these laws by measuring the air quality and conducting covert observations at 154 hospitality venues (HVs) in Addis Ababa. Indoor air quality was measured using Dylos air quality monitors during the peak hours of the venues, with concentrations of particulate matter <2.5 microns in diameter (PM2.5) used as a marker of second-hand tobacco smoke. A standardized checklist was used to assess compliance with smoke-free laws during the same peak hours. The average PM2.5 concentrations were classified as good, moderate, unhealthy for sensitive groups, unhealthy for all, or hazardous using the World Health Organization's (WHO) standard air quality index breakpoints. Only 23.6% of the venues complied with all smoke-free laws indicators. Additionally, cigarette and shisha smoking were observed at the HVs. Overall, 63.9% (95% confidence interval: 56-72%) of the HVs had PM2.5 concentrations greater than 15 µg/m3. The presence of more than one cigarette smoker in the venue, observing shisha equipment in the indoor space, and the sale of tobacco products in the indoor space were significantly associated with higher median PM2.5 concentration levels (p < 0.005). Hazardous level of PM2.5 concentrations-100 times greater than the WHO standard-were recorded at HVs where several people were smoking shisha and cigarettes. Most HVs had PM2.5 concentrations that exceeded the WHO average air quality standard. Stricter enforcement of smoke-free laws is necessary, particularly for bars and nightclubs/lounges.