Measured PM10 Concentrations Research Articles

Mapping air quality variations in industrial and agricultural zones: Understanding spatial and temporal patterns of selected air pollutants

With the escalating threat of air pollution on the biota, the need for research on pollutants has become imperative in industrial, urbanized and heavily populated cities. This study aimed to determine the level of major outdoor atmospheric pollutants (PM10, PM2.5, TVOCs, and HCHO) in specific locations including industrial and agricultural zones. Field measurements for this research, spanning 52 locations, were collected in the spring season using a portable instrument, and spatial variation maps were developed using ArcGIS software. The study measured PM10 concentrations ranging from 31 μg/m3 to 267 μg/m3 and PM2.5 concentrations ranging from 27 μg/m3 to 253 μg/m3. PM2.5 levels at most sites exceeded USEPA, WHO, and PEQS standards, while PM10 levels generally met these standards. The highest values of coarse and fine particulate matter were observed in areas dominated by emissions from the textile industry. Furthermore, particulate matter concentrations showed spatial variations, increasing towards the North and East directions. The Total Volatile Organic Compounds (TVOCs) concentrations, ranging from 0 mg/m3 to 4.019 mg/m3, exceeded Hong Kong SAR standards at five locations, thereby elevating health risks for residents in these areas. Similarly, formaldehyde levels, ranging from 0 mg/m3 to 0.754 mg/m3, surpassed the Hong Kong SAR standard at seven locations, indicating a substantial threat of exposure for the local population. In general, air quality at most field sampling sites was registered as UNQUALIFIED but was also observed to be FRESH at a few locations. This research is significant for understanding the levels of major pollutants so that the concerned authorities can take appropriate measures to tackle air pollution by introducing pollution control technologies in industries, switching to renewable energy options, and managing road dust.

Application of aggregation operators for forecasting [formula omitted] fluctuations: From available Caribbean data sites to unequipped ones

Air pollution is a substantial issue for public health. Predicting the levels of airborne particles, especially those originating from natural phenomena like sand mists from the African dust belt, enables more effective warnings for affected populations. Currently, there are only three islands that continuously measure PM10 concentrations in the Caribbean area. Thus, there is a main issue for the forecast of these particulate pollutants for unequipped sites. In this article, a new approach based on the use of aggregation operators in a non-fuzzy framework allows to predict the quantity of PM10 for certain sites in this geographical area. It is the characteristics of non-linearity and downward reinforcement of the fuzzy operators used (triple Pi and t-norms) which make it possible to model the evolution of a phenomenon as complex as the sand mists from African dust belt. The time series having been normalized, the tests were carried out on two of the three sites (Martinique and Guadeloupe) to predict the values of the third site (Puerto Rico) and assess the quality of the prediction. The mean absolute error (MAE), the mean bias error (MBE), the root mean square error (RMSE) and the index of agreement (IOA) values show that this approach provides significant results. The African dust seasonality strongly impacts these performances metrics. These initial promising findings emphasize that the utilization of aggregation operators is a robust method for forecasting PM10 fluctuations, eliminating the need for complex parameterization or learning phase that would otherwise entail significant computational costs.

Modelled atmospheric concentration of tyre wear in an urban environment

Microplastics are emitted to the environment from many sources, and transported via water, soil and air. The airborne component is less well studied and measurements often focus on particles larger than 10 μm, while smaller particles are worse from a health perspective and likely more abundant. Measurement studies do often not include tyre wear, which is a large source of microplastics in urban areas. To improve the understanding of the microplastics concentration in urban areas, simulations of emissions and concentrations of atmospheric tyre wear particles (TWP) over Stockholm are performed. The results show that TWP are ubiquitous in urban areas, with highest concentrations along busy highways and in poorly ventilated street canyons. Yearly average roof level concentrations of TWP-PM10 range from 0.2 μg m−3 in areas with moderate traffic to 1.2 μg m−3 close to busy highways. Average yearly concentrations at three selected street canyons range from 0.7 to 1.1 μg m−3. The modelled concentrations correspond to 4–6% of the total measured PM10 concentration. TWP emissions are expected to increase with increasing traffic in future, and possibly also with the increase in vehicle weight with electric vehicles and SUV:s.

Sensor based real time air pollutants monitoring for an urban industrial area


 
 
 
 Introduction: This study used economical sensors and an Unmanned Aerial Vehicle (UAV) to examine real-time air pollution assessment for an urban industrial area.
 
 
 
 
 Materials and methods: Using a DJI Phantom 3 Pro, the concentrations in the research area were measured. In the study, Carbon monoxide (CO) and Nitrogen dioxide (NO2) were measured using metal oxide sensors, and Particulate Matter (PM10) was determined using a dust smoke particle sensor. Pollutants were measured at heights of 0.8 m and 10 m for a period of one month.
 
 
 
 
 Results: With an increase in elevation, a gradual drop in pollutant concentration has been seen. High traffic volumes and fuel combustion are to blame for this increase. The concentration of CO, NO2 and PM10 at 0.8 m has been found to be 22.53%, 42.90% and 45.86% respectively higher when compared at 10 m. The main finding of this study is the use of an UAV integrated with sensors for vertical monitoring of the pollutant concentration.
 
 
 
 
 Conclusion: The CO concentration was found to be less than the standard value but near to it, when the data were compared to the Central Pollution Control Board (CPCB) standards. While it was discovered that the measured PM10 concentration was higher than the CPCB standard value, the observed NO2 concentration was determined to be lower than the standard value. Also, given that they produce satisfactory results, low-cost gas sensors can be employed to conduct concentration measurement studies.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Estimation of Respiratory Disease Burden Attributed to Particulate Matter from Biomass Burning in Northern Thailand Using 1-km Resolution MAIAC-AOD

The upper northern Thailand suffers from air pollution due to open burning, which has been known for a long time. It was also found that different respiratory diseases were attributed to air pollution, especially particulate matter. This study estimated the health impacts attributed to PM10 between 2014 and 2016 using the burden of disease in terms of the disability adjusted life year (DALYs). The spatial correlation was evaluated based on applicable remote sensing data using the geographically weighted regression (GWR) model. The average measured PM10 concentrations for the summer and annual periods between 2014 and 2016 were 73 and 89 µg m-3, respectively, exceeded the national standard (50 µg m-3). In the months of March and April, when PM10 concentrations were at their highest, the maximum values of the Multi-Angle Implementation of Atmospheric Correction (MAIAC-AOD), 2.70 and 3.48, were recorded. There was a strong correlation between the MAIAC-AOD and the ground-based AOD measurements (AERONET stations), with R of 0.8468, 0.8396, and 0.8334 between 2014–2016. The correlation coefficients for the 3,208 co-located gridded of PM10 emissions vs. measured PM10, measured PM10 vs. MAIAC-AOD, and MAIAC-AOD vs. PM10 emissions were 0.6656, 0.6446, and 0.5580, respectively. The spatial correlation between the interpolated measured PM10 and 1-km MAIAC-AOD was 0.5979, 0.3741, and 0.7584 as an outcome of GWR. The total DALYs of chronic obstructive pulmonary disease (COPD) attributable to PM10 in 2014–2016 were 115,930 years per 100,000 population, with the relative risk of COPD related to PM10 at a 95% confidence interval of 1.2045–1.2107.

Laboratory evaluation of the Alphasense OPC-N3, and the Plantower PMS5003 and PMS6003 sensors

Ambient levels of particulate matter (PM) are linked to numerous adverse health effects. However, fewer studies have evaluated the effects of coarse PM (larger than 2.5 μm in diameter), and ambient measurements of coarse PM are particularly sparse. Although low-cost PM sensors have been used to complement regulatory measurements of PM2.5, many of these sensors, such as the Plantower PMS5003, are ineffective in measuring coarse PM. The Alphasense OPCs have shown promise in detecting coarse PM and have been used in the field to measure PM10 concentrations. These field evaluations have identified inter-sensor variability. Although field evaluation is critical for understanding sensor performance under environmentally relevant conditions, it provides limited information about sensor response characteristics, which are essential for determining the factors that may affect sensor measurements and contribute to inter-sensor variability. This study aims to understand these factors by conducting a size-selectivity study using monodisperse particles, and evaluating the effect of instrument-specific properties, like flow rate and laser strength, on the sensor-reported sizes and number counts. This study also evaluates a common low-cost sensor, the Plantower PMS5003, and a newer version, the PMS6003, for size selectivity. Monodisperse dioctyl sebacate particles of various diameters (2, 3, 5, 6, 9, and 10 μm) were generated using a flow-focusing monodisperse aerosol generator, and the performance of nine different OPCs was evaluated. For all sizes tested, the nine OPC-N3s detected the particles, showed a peak near the target diameter, and exhibited some inter-sensor variability. The four PMS5003s and four PMS6003s detected all particle sizes but assigned all particles to the smallest size bin i.e., 0.35 μm–1 μm. With an aerodynamic particle sizer (APS) as a reference, the OPCs showed a positive bias for mean particle diameter and a coefficient of variance (CV) of less than 10%. For number concentration, the OPCs showed a negative bias, compared to the APS, and inter-sensor variability increased with the particle diameter. The laser wavelength of the OPC-N3s varied between 600 and 650 nm and appeared to have some effect on inter-sensor variability of the mean size. The flow rate reported by the OPC appeared to affect the inter-sensor variability in the number concentration.

The Use of Multilayer Perceptrons to Model PM2.5 Concentrations at Air Monitoring Stations in Poland

The biggest problem facing air protection in Poland is the high levels of suspended particular matter concentrations. Air monitoring reports show that air quality standards, related to PM10 and PM2.5 concentrations, are exceeded every year in many Polish cities. The PM2.5 aerosol fraction is particularly dangerous to human and animal health. Therefore, monitoring the level of PM2.5 concentration should be considered particularly important. Unfortunately, most monitoring stations in Poland do not measure this dust fraction. However, almost all stations are equipped with analyzers measuring PM10 concentrations. PM2.5 is a fine fraction of PM10, and there is a strong correlation between the concentrations of these two types of suspended dust. This relationship can be used to determine the concentration of PM2.5. The main purpose of this analysis was to assess the accuracy of PM2.5 concentration prediction using PM10 concentrations. The analysis was carried out on the basis of long-term hourly data recorded at several monitoring stations in Poland. Artificial neural networks in the form of a multilayer perceptron were used to model PM2.5 concentrations.

Evaluation of the effects of particulate matter forecasting system on behavior patterns in Seoul, Korea

Background and aim: The particulate matter (PM) forecasting system is one of the most prominent political options that can help to reduce the risk of PM. Its goal is to urge people to take avoidance behaviors for upcoming pollution by issuing warnings. In Korea, the national PM10 forecasting system was launched on February 6, 2014. We aimed to estimate the effectiveness of the PM forecasting system in relation to the number of visitors to the Children’s Grand Park in Seoul, Korea. Methods: We used regression discontinuity design to estimate the effectiveness of the policy from 2014 to 2019. Since the PM forecasting results were not available, the measured PM10 concentration of Seoul was used as a proxy for treatment variable. The cut-off point of the intervention was set to 80ug/m3, the starting point for a "Bad” rating, and the bandwidth was set to 10ug/m3. We further adjusted the daily mean temperature. Results: When the PM10 concentration exceeds 80ug/m3, the number of visitors to the park decreased by about 15,000. In addition, there was a negative relationship between PM10 concentration and the number of park visitors where pollution levels were over 80ug/m3. The results were statistically significant at the 10% significance level. Conclusions: Our result indicates that the PM10 forecasting system seems effective in Korea. According to previous studies in Korea, the risk of PM10 on various health outcomes decreased in recent years. The introduction of the forecasting system and increment in awareness of PM might be possible causes for that change in the PM10 risk.

SCAMPER Monitoring Platform to Measure PM10 Emission Rates from Unpaved Roads in Real-Time

The SCAMPER method for measuring PM10 emission rates from roadways was used to evaluate mitigation methods for public unpaved roads and a treated mine haul road. The SCAMPER method uses a small trailer to measure PM10 concentrations behind a vehicle at a point that is representative of the mean PM10 concentration in the vehicle’s wake. This concentration multiplied by the frontal area has been shown to be a reasonable estimate of the emission rate in units of grams per meter traveled. On public roads it was towed by a 2006 Ford Expedition and on a mine haul road it was towed behind both the Expedition and an earth mover weighing over 150 tons fully loaded. Since the SCAMPER is capable of measuring emission rates on both paved and unpaved roadways, a direct comparison of the effectiveness of mitigation methods with respect to a similar paved road was possible.

African dust particles over the western Caribbean – Part I: Impact on air quality over the Yucatán Peninsula

Abstract. On a global scale, African dust is known to be one of the major sources of mineral dust particles, as these particles can be efficiently transported to different parts of the planet. Several studies have suggested that the Yucatán Peninsula could be influenced by such particles, especially in July, associated with the strengthening of the Caribbean low-level jet. Although these particles have the potential to significantly impact the local air quality, as shown elsewhere (especially with respect to particulate matter, PM), the arrival and impact of African dust in Mexican territory has not been quantitatively reported to date. Two short-term field campaigns were conducted to confirm the arrival of African dust on the Yucatán Peninsula in July 2017 and July 2018 at the Mérida atmospheric observatory (20.98∘ N, 89.64∘ W). Aerosol particles were monitored at ground level using different online and off-line sensors. Several PM2.5 and PM10 peaks were observed during both sampling periods, with a relative increase in the PM levels ranging between 200 % and 500 % with respect to the normal background conditions. Given that these peaks were found to be highly correlated with supermicron particles and chemical elements typically found in mineral dust particles, such as Al, Fe, Si, and K, they are linked with African dust. This conclusion is supported by combining back trajectories with vertical profiles from radiosondes, reanalysis, and satellite images to show that the origin of the air masses arriving at Mérida was the Saharan Air Layer (SAL). The good agreement found between the measured PM10 concentrations and the estimated dust mixing ratio content from MERRA-2 (Version 2 of the Modern-Era Retrospective analysis for Research and Applications) corroborates the conclusion that the degradation of the local (and likely regional) air quality in Mérida is a result of the arrival of African dust.

Long-term Characterization of Urban PM10 in Hungary

Over urban areas in Hungary, the annual average PM10 concentrations are not frequently higher than 40 µg m–3. Despite the mitigation efforts of the local governments, the annual number of exceedances of the daily limit of 50 µg m–3 is higher than what is outlined in EU Directive No 2008/50/EC. The goal of the present study is to assess the characteristics of the temporal (annual, seasonal, daily) variations in PM10 concentrations in selected Hungarian cities with large populations, where most of the exceedances have been reported. The impacts of meteorological conditions on the measured PM10 concentrations and their temporal variations are also evaluated. An important aspect of studying the trends of air pollution is that the tendencies depend not only on the emissions of certain pollutants but also on the meteorological conditions in the area of interest. To analyse emission-related trends, the meteorological signal must be removed from the data series. In this study, the Kolmogorov-Zurbenko (KZ) filter was used for this type of trend separation. Moreover, multiple nonlinear regression analysis was used to find relationships between the PM10 concentration and several meteorological parameters. The goal of this analysis is to estimate the expected daily mean PM10 concentration values. The results of this analysis demonstrate that the regression equation can provide an adequate method for PM pollution forecasting. In addition to the hourly PM10 concentrations and basic meteorological data, global radiation and boundary layer height were considered in the characterization process.

Modelling daily PM10 concentrations at high spatio-temporal resolution across South Africa.

Background Air pollution is a major environmental risk to health, responsible for one in every nine deaths globally. Currently, limited spatiotemporal air pollution data is available to conduct large-scale epidemiological studies investigating the adverse health effects of air pollution in South Africa. Here we aim to model South Africa’s daily average PM10 concentrations for years 2010 to 2016 using spatial and temporal predictor variables including South Africa’s data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Copernicus Atmosphere Monitoring Service (CAMS). Methods We followed a two-stage modelling strategy. In stage 1, we imputed missing Aerosol Optical Depth (AOD) data, due mainly to cloud cover, by developing Random Forest (RF) models between AOD and CAMS AOD estimates also including additional large-scale spatiotemporal predictors. In stage 2, we developed RF models to explain the measured PM10 concentration using the imputed AOD data from stage 1 and spatiotemporal predictors including land cover, road density, population density, altitude, climatological zones, and meteorological variables. Results Preliminary analysis for 2016 shows that our stage 1 RF models can explain 96 percent in variability in the AOD data. In stage 2, our models explain 79% of the variation in measured ground level PM10 concentrations with the corresponding 10-fold cross-validation R2 of 78%. The RF stage 2 model includes variables depicting some PM10 source related variables like day of the week, geographical coordinates, road density, altitude and meteorological predictor variables in the top 10 of influential predictor variables. Conclusion This is the first study showing the potential of earth observation data to develop models explaining fine scale (1x1 km) temporal (daily) variation of PM10 across South Africa. We developed models for years 2010 to 2016. The predicted PM10 concentrations will be made available to facilitate health research in South Africa.

Prediction models using outdoor environmental data for real-time PM10 concentrations in daycare centers, kindergartens, and elementary schools

Children spend a considerable amount of time in daycare centers, kindergartens, and elementary schools. Poor indoor air quality (IAQ) in the educational facilities can affect the health of the children and impair their academic performance. The prediction of real-time PM10 concentration could be useful to intervene the problem of poor IAQ. This study developed models to predict real-time indoor PM10 concentration in the daycare centers, kindergartens, and elementary schools using outdoor environmental data. Indoor PM10 concentrations were measured in 54 daycare centers, 12 kindergartens, and 21 elementary schools in Seoul, South Korea, using a real-time monitor (AirGuard K) over a period of one year. Multiple linear regression models were used to predict real-time indoor PM10 concentration in these educational facilities using outdoor PM10 and meteorological data as input variable. Four formations (original, ratio of indoor-to-outdoor, root-transformation, and log-transformation) for dependent variable were compared to determine the best performance of the model. A 10-fold cross-validation method was used to evaluate the accuracy of the prediction models. Daycare centers showed the highest indoor PM10 concentration. Root-transformed models with high accuracy were developed to predict the real-time indoor PM10 concentration in educational facilities every 10 min. The R2 of the prediction models were 0.64 in the daycare centers, 0.45 in the kindergartens, and 0.43 in the elementary schools. The 24 h profile of the predicted indoor PM10 was similar to the measured PM10 concentration. The prediction models could provide real-time PM10 levels in educational facilities without direct indoor measurement and observation.

Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value

In Nantes, low-cost sensors were installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM10 concentrations. This work aims to use the large amount of observations provided by the sensors for air quality mapping at the urban scale in order to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations. A preprocessing is applied to the raw sensor dataset to remove the unreliable observations based on an outlier detection technique and to compensate for the measurement drift by adjusting the estimated daily variation of the underlying background PM10 concentrations with the reference stations. Then, data fusion is performed by combining the preprocessed fixed and mobile low-cost sensor observations and the 2016 annual average of the ADMS-Urban outputs. The measurement uncertainty related to the low-cost sensors and the dispersion of the data are considered in data fusion as the Variance of Measurement Error (VME). The spatial interpolation is achieved at hourly resolution and results are presented for November 29th, 2018 from 7 am to 7 pm. Hourly fused maps show disparate responses to data fusion mainly depending on the variability of the sensor data and the correlation between the sensor observations and the drift. The data fusion performance has been investigated by comparing the daily average of the estimated concentrations, the reference observations and the hourly model outputs at each station of the Air Pays de la Loire network. Results show that considering the model alone implies 8% bias whereas including the LCS observations reduces the bias to 2.5%. However, the concentration distributions related to the data fusion are characterized by a lower dispersion than the reference observations and the model estimation. Thus, the fusion smooths the PM10 peaks. In addition, the effect of the measurement uncertainty has been investigated by doubling it or reducing it to the reference station measurement uncertainty. The sensitivity study demonstrates that the performance is increasing by reducing the uncertainty. This highlights the importance to estimate accurately the measurement uncertainty of the devices to ensure relevant air quality mapping. The method efficiency is also quite limited by the low correlation between the sensor observations and the model used as external drift in the kriging that may be explained by the remaining bias on LCS data. Efforts on this issue might increase the performance of the spatial interpolation.

Monitoring Air Quality and Estimation of Personal Exposure to Particulate Matter Using an Indoor Model and Artificial Neural Network

Exposure to particulate materials (PM) is known to cause respiratory and cardiovascular diseases. Respirable particles generated in closed spaces, such as underground parking garages (UPGs), have been reported to be a potential threat to respiratory health. This study reports the concentration of pollutants (PM, TVOC, CO) in UPGs under various operating conditions of heating, ventilation and air-conditioning (HVAC) systems using a real-time monitoring system with a prototype made up of integrated sensors. In addition, prediction of the PM concentration was implemented using modeling from vehicle traffic volumes and an artificial neural network (ANN), based on environmental factors. The predicted PM concentrations were compared with the level acquired from the real-time monitoring. The measured PM10 concentrations of UPGs were higher than the modeled PM10 due to short-term sources induced by vehicles. The average inhalable and respirable dosage for adult was calculated for the evaluation of health effects. The ANN predicted PM concentration showed a close correlation with measurements resulting in R2 ranging from 0.69 to 0.87. This study demonstrates the feasibility of the use of the air quality monitoring system for personal-exposure to vehicle-induced pollutant in UPGs and the potential application of modeling and ANN for the evaluation of the indoor air quality.

Real-time PM10 emission rates from paved roads by measurement of concentrations in the vehicle's wake using on-board sensors part 1. SCAMPER method characterization

Abstract Based on emission factors derived from the AP-42 algorithm, particulate matter from paved roads has been estimated to be a major source of PM10 of geologic origin. This is an empirical formula based on upwind-downwind measurement of PM10 concentrations and is dependent solely on the silt loading of the pavement and the weight of vehicles. A number of upwind-downwind studies conducted in urban areas to validate this algorithm have been generally inconclusive because the PM10 concentration difference between upwind and downwind often is within the measurement uncertainty. In the approach presented here PM10 concentrations were measured directly behind a moving vehicle in order to improve the measurement sensitivity for estimating the emission rates for vehicles on paved roads. Optical sensors were used to measure PM10 concentrations with a time resolution of approximately 10 s. Sensors were mounted in the front of the vehicle and behind it in the well-mixed wake. A special inlet probe was designed to allow isokinetic sampling under varying speed conditions. As a first approximation the emission rate was calculated by multiplying the PM10 concentration difference between the front and rear of a moving vehicle by the frontal area of the vehicle. This technique is also useful for quickly surveying large areas and for investigating hot spots on roadways caused by greater than normal deposition of PM10 forming debris. The method is designated as SCAMPER: System for the Continuous Aerosol Measurement of Particulate Emissions from Roads. Part I describes SCAMPER development and Part II describes a comprehensive field testing of mobile methods.

Validation of Low-Cost Sensors in Measuring Real-Time PM10 Concentrations at Two Sites in Delhi National Capital Region.

In the present study, we assessed for the first time the performance of our custom-designed low-cost Particulate Matter (PM) monitoring devices (Atmos) in measuring PM10 concentrations. We examined the ambient PM10 levels during an intense measurement campaign at two sites in the Delhi National Capital Region (NCR), India. In this study, we validated the un-calibrated Atmos for measuring ambient PM10 concentrations at highly polluted monitoring sites. PM10 concentration from Atmos, containing laser scattering-based Plantower PM sensor, was comparable with that measured from research-grade scanning mobility particle sizers (SMPS) in combination with optical particle sizers (OPS) and aerodynamic particle sizers (APS). The un-calibrated sensors often provided accurate PM10 measurements, particularly in capturing real-time hourly concentrations variations. Quantile–Quantile plots (QQ-plots) for data collected during the selected deployment period showed positively skewed PM10 datasets. Strong Spearman’s rank-order correlations (rs = 0.64–0.83) between the studied instruments indicated the utility of low-cost Plantower PM sensors in measuring PM10 in the real-world context. Additionally, the heat map for weekly datasets demonstrated high R2 values, establishing the efficacy of PM sensor in PM10 measurement in highly polluted environmental conditions.

Numerical Simulation of a Severe Dust Storm over Ahvaz Using the HYSPLIT Model

In this paper, numerical simulation of a severe dust storm over Ahvaz on 16–18 June 2016 was performed using the HYSPLIT. To that end, the HYSPLIT model has been configured for different model setups to determine the most desirable suite of parameters to represent the measured concentrations better. Among several model parameters, meteorological dataset (GDAS1.0 and GDAS0.5), friction velocity (P10F) and particle numbers (NumPar) were detected as the most influential parameters on the simulation results. Accordingly, with P10F = 0.6, NumPar = 5000, and GDAS1.0 configurations, the model has reproduced the most precise results, and the correlation coefficient between the measured and the simulated PM10 concentration was as high as 0.9. Since the threshold friction velocity influences the results significantly, it is suggested to calculate the P10F coefficient for each distinct dust storm meticulously. Once the best model parameters have been obtained, the HYSPLIT has been run for 33 possible sources separately to estimate the contribution from each sub-region to the levels of the measured PM10 concentration of Ahvaz during 16–18 June 2016. According to the results, central Iraq, northern Syria, western Iraq, and Al-Hawzieh/Al-Azim wetland account for 71, 19, 6, and 4%, respectively.

The applicability of low-cost PM10 sensors for atmospheric air quality monitoring

Described in this work are the results of field tests carried out in the Tricity Agglomeration between 01 April 2018 and 30 June 2018 in order to evaluate the usefulness of low-cost PM10 sensors in atmospheric air quality monitoring. The results were juxtaposed with the results obtained using reference methods. The results were validated based on the measurement uncertainty as described in the EU report "Demonstration of Equivalence of Ambient Air Monitoring Methods. EC Working Group on Guidance for the Demonstration of Equivalence". Moreover, the impact of external factors (humidity, pressure, temperature) on the obtained results was also assessed. It was shown that the low-cost sensors display measurement uncertainty which exceeds the acceptable values as compared to the reference methods and correction factors depending on the measured PM10 concentration need to be introduced in order to fulfil the criteria of equivalence.

Assessment of Inhalable Particulate Matter Associated with a Refinery in Curaçao

Inhalation and atmospheric pollution studies have focused on particulate matter due to correlations and associations with various morbidities and mortalities. This research analyzed ambient concentrations of inhalable particulate matter (PM10) on the island of Curaçao in order to evaluate through comparative literature analysis and recommended public health guidelines the potential health risks. Available hourly, daily and monthly PM10 measurements were accessed from June 2010 through December 2014 from a local air monitoring station in Willemstad. Mean annual concentrations of PM10 (31 - 122 μg/m3) in Curaçao are among the highest reported globally, demonstrating an increasing trend over time and exceed current public health guidelines recommended by local and international agencies. While the epidemiological evidence is inadequate to infer a causal association between health effects and long-term exposures of the measured PM10 concentrations, the results indicate that emissions controls are not adequate for compliance with international exposure standards.