Fine Dust Concentration Research Articles

Experimental study of the effects of a particle filter in front of automotive radiator on its cooling performances and cooling drag

According to the World Health Organization (WHO), air pollution is the single greatest worldwide health risk especially in urban environments. In these areas, the vehicle traffic significantly contributes to the fine dust concentration in the ambient air. While the current legislation focuses mainly on the exhaust emission of vehicles, 85% of the overall fine dust emissions originating from brakes, tires, and road abrasion are not yet regulated. In this context, an integrated particulate filter system fitted into previously unused installation spaces in frontend of a vehicle in front of the radiator allows to compensate a part of the particle emissions of a vehicle try to reach the neutral emission. Add filter is not without consequences for the cooling performances. This study aims to assess the impact of the particulate filter system on the radiator cooling performances and cooling drag coefficient. An experimental setup was developed to characterize an automotive radiator with and without particle filter in terms of power and velocity distribution. Cooling capacity of the radiator depends clearly of the airflow through the radiator. Filter increases the pressure drop and decreases airflow. Hence, the heat power exchange can be reduced up to 10% for a pressure drop level almost twice. The airflow inlet inside engine compartment is not beneficial for aerodynamic aspect. By increasing pressure drop, the cooling drag coefficient can be reduced up to 10% when a filter covers entirely the radiator surface. The results indicate that the filter affects the cooling capacity of the radiator, which can require modifications to the cooling system strategy.

Investigating the Occurrence of Dust Storms and its Zoning Using GIS During 2018-2021: A Case Study in Ahvaz, Iran

Background: A dust storm is a critical environmental issue and natural hazard in arid and semiarid regions worldwide. Particulate matters including PM10 and PM2.5 are of the primary air pollutants, which can adversely affect human health. This research aimed to determine the fine dust concentration spatiotemporally and estimate the suspended particulate matter (PM10 and PM2.5) during 2018-2021 in Ahvaz. Methods: An analytical- descriptive method and the data from synoptic weather stations in the studied area were used in this study. The zoning of dust was done using the kriging method by ArcGIS software. Results: The results indicated that the year 2021 experienced the highest frequency of dusty days, with 54 occurrences, while the year 2019 exhibited the lowest frequency, with only 28 dusty days. Notably, Ahvaz city recorded the highest dust concentration in 2020, reaching an average of 4667 mg/m3 . Based on the findings of PM concentration over the four-year period, it is evident that 2021 recorded the highest PM10 concentration (326.14 mg/m3 ), while 2020 exhibited the highest PM2.5 concentration (148.26 mg/m3 ). Conclusion: The results revealed that there was an increase in dust concentration in the east and north. This is probably attributed to the proximity to dust centers.

Effect of GHX02 on an Asthma-Rhinitis Mouse Model Induced by Ovalbumin and Diesel Particulate Matter.

Fine dust concentrations come in direct contact with the human respiratory system, thereby reducing lung function and causing respiratory diseases such as asthma and rhinitis. The aim of this study was to evaluate the efficacy of GHX02 (combination of four herbs [Trichosanthes kirilowii, Prunus armeniaca, Coptis japonica, and Scutellaria baicalensis]), a herbal extract with established efficacy against bronchitis and pulmonary disease, in the treatment of asthma accompanied by rhinitis aggravated by fine dust. Therefore, we constructed an asthma-rhinitis mouse model of Balb/c mice challenged with ovalbumin (OVA) and fine diesel particulate matter, which were administered with three concentrations of GHX02. GHX02 significantly inhibited the increase of total cells and immune cells in bronchoalveolar lavage fluid, lung tissue, and nasal ductal lymphoid tissue (NALT). GHX02 also reduced the severity of histological lung injury and the expression of interleukin (IL)-1α and nuclear factor kappa B (NF-κB), which regulate inflammatory responses. The results indicate that GHX02 inhibited the inflammatory immune response in mice. Therefore, this study highlights the potential of GHX02 as a treatment for patients with asthma accompanied by rhinitis. Balb/c mice were challenged with OVA and PM10D, and then treated with three concentration of GHX02. GHX02 significantly inhibited the increase of total cells, immune cells lymphocytes, neutrophils, and macrophages, as well as their expression in lung tissue. GHX02 significantly inhibited the increase of total cells and immune cells in NALT. GHX02 decreased the severity of histological lung injury, expression of IL-1α and NF-κB. This study suggests the probability that GHX02 is effective for asthma patients with rhinitis by inhibiting inflammatory immune response.

МЕТОДОЛОГИЯ ПРИНЯТИЯ ОПТИМАЛЬНЫХ РЕШЕНИЙ ПО УПРАВЛЕНИЮ ЖИЗНЕННЫМ ЦИКЛОМ ОБЪЕКТА КАПИТАЛЬНОГО СТРОИТЕЛЬСТВА С УЧЕТОМ КРИТЕРИЯ ПЫЛЕВОГО ЗАГРЯЗНЕНИЯ АТМОСФЕРНОГО ВОЗДУХА

Abstract. The existing approaches to decision-making in construction have two significant drawbacks. Firstly, they allow us to calculate the effectiveness of the options under consideration only for the object construction phase, without giving an idea of the decisions effectiveness taken at other life cycle stages. Secondly, the absence of an environmental safety criterion for a particular technology in the model reduces its universality and the adequacy of the decisions taken.
 The article proposes a new approach to management decision-making in construction as to the management of a capital construction object life cycle. A methodology has been developed for making optimal decisions on life cycle management, taking into account the criterion of atmospheric air dust pollution. In order to build the methodology, a block diagram of the formation of dust emissions into the atmosphere from temporary pollution sources at various stages was developed. The dust pollution values for works performed at various life cycle stages of a capital construction object were obtained experimentally. Based on the data obtained, the values of the given optimization criterion are determined for choosing the optimal solution for life cycle management, taking into account the criterion of atmospheric air dust pollution.
 Unlike the existing ones, the developed methodology allows, firstly, to calculate the effectiveness of the options under consideration not only for the construction stage, but also for the entire object life cycle. Secondly, the model introduces a criterion of environmental safety of the control solutions under consideration, quantified by the degree of dust pollution of atmospheric air.
 Subject of research: The processes of atmospheric air dust pollution when performing work at various stages of the capital construction object life cycle. 
 Materials and methods: Theoretical and experimental methods for determining the concentration of dust pollution using the Ecologist program, based on the results of field dust pollution measurements and aerosol dispersed composition analysis.
 Results: A methodology for making optimal decisions on managing the capital construction object life cycle has been developed, including 5 stages.
 1. Identification of work types that cause atmospheric air dust pollution.
 2. Determination of fine dust concentration from each type of work.
 3. Determination of the atmospheric air pollution indicator total value for the implementation of all control solutions (technologies) variants and for all capital construction object life cycle stages according to the proposed model.
 4. Determination of given optimization criterion values for the implementation of all control solutions (technologies) variants and for all life cycle stages according to the proposed expression.
 5. Selection of the control solution (technology) with the lowest given optimization criterion value.
 Conclusions: As a result of theoretical and practical research, a methodology has been created that allows, firstly, to calculate the effectiveness of the options under consideration not only for the construction stage, but also for the entire object life cycle. Secondly, the model introduces a criterion of environmental safety of the control solutions under consideration, quantified by the atmospheric air dust pollution degree.

Development of Machine Learning-Based Energy Management Agent to Control Fine Dust Concentration in Railway Stations

Development of Machine Learning-Based Energy Management Agent to Control Fine Dust Concentration in Railway Stations

Justification of an Energy-Efficient Air Purification System in Subways Based on Air Dust Content Studies

It is not uncommon that subways count as densely populated areas, so air quality standards, including fine dust concentration, have been established for them. As passengers and subway staff are exposed to potentially harmful airborne particles, addressing this issue is vital to ensuring a safe and healthy environment on the subway. To reduce the dust concentration in subway systems, the authors propose installing filters to capture dust in ventilation failures between subway tunnels near metro stations. A novel aspect of the proposed method is the fact that airflow will be moved through filters by using the piston action of trains passing through the tunnels. The result of this research provides empirical evidence regarding dust content and mass concentrations of PM2.5 and PM10 in subway environments. While some existing literature discusses air quality in subways, the inclusion of specific measurements and data from the experiment strengthens the understanding of the severity of dust-related air quality issues in such environments. The data for this study were collected in the Almaty subway (Republic of Kazakhstan) at four stations: Raiymbek Batyr, Almaty, Baikonur and Alatau. Measuring points were located on passenger platforms, in the halls and at the entrances to the station. The lab scale tests determined the percentage of particles by their diameters relative to the total volume of dust, the percentage of dust particles smaller than a certain diameter, the percentage of various metal oxides and the average dust density. A preliminary energy assessment has been done on the proposed method of air purification from dust. With a frequency of 24 pairs of trains per hour, the energy savings per ventilation failure will be 240.170 kWh.

Analysis of Flow Characteristics of Window-Combination-Type Ventilation System Using CFD

In this study, we analyze the performance of ventilation modules to improve air quality in educational facilities. Using (CFD), we examine the flow design variables of a window-mounted ventilation module. Using computational analysis, we analyze various flow design characteristics of window-mounted ventilation modules and review optimal conditions. First, we measure the carbon dioxide concentration in the classroom and use CFD to analyze the internal air characteristics according to the ventilation module’s inflow speed, inflow angle, and indoor temperature conditions. According to classroom air quality management standards, the concentration of carbon dioxide must be managed below 1000 ppm. When the ventilation module’s inflow velocity was 2.0 m/s, a carbon dioxide concentration of less than 1000 ppm was measured in the classroom. Additionally, an air filter was selected to prevent the inflow of external fine dust through the ventilation module. The suitability of HEPA H14 was reviewed to design the weight concentration of fine dust flowing from the ventilation module to be less than 50 μg/m3. Through research, flow design conditions for a window-mounted ventilation module were presented to reduce carbon dioxide concentration inside the classroom. The analysis of the ventilation system flow characteristics proposed in this study derived primary data for improving the classroom ventilation system.

Assessment of the impact of local climate zones on fine dust concentrations: A case study from Hanoi, Vietnam

Despite the growing importance of particulate matter (PM) air pollution its assessment still remains often limited to only a few point measurements, making it difficult to assess the spatial extent. This research shows that under lower pollution conditions (<50 ppm) the accuracy of low cost sensors is too low to show meaningful spatial differences. However under high pollution conditions, when air quality information is also most needed, they perform quite well, after calibration (precision 10 ppm).With a gradient boosting model (GBM) the relationship between PM2.5 as response variable and relative humidity (%), temperature (°C), windspeed (km/h), precipitation (mm) and Local Climate Zones (LCZ's) as predictor variables was explored. The last variable, LCZ's being a method to classify areas based on their land cover and present building types.If the LCZ's variable would show to be a good predictor variable this could make mapping PM easier as there is less data available on the spatial distribution of PM. And its direct link to urban morphology could prove to be helpful in the process of urban planning.The GBM showed that relative humidity (RH) had the highest relative influence (34.8%.) on PM2.5 concentrations followed by windspeed (23.6%), LCZ's (21.4%) and temperature (20.1%).Especially the so-called “compact built” LCZ's appeared to have a buffering impact on the observed PM2.5 concentrations, most likely because of their narrower roads and associated lower traffic load. These LCZ's are marked by a dense arrangement of buildings with not many, if any, natural elements present.

Comparison of polymeric components and tire wear particle contents in particulate matter collected at bus stop and college campus

Particulate matter (PM2.5) samples were collected at two different places of a college campus (CC) and a bus stop (BS) nearby the college campus. The traffic volume of college campus was very low due to untact classes. Polymeric components and tire wear particle (TWP) contents in the PM2.5 samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Various polymeric components such as natural rubber (NR), bitumen, saturated hydrocarbons, poly(ethylene terephthalate) (PET), and plant-related particles (PRPs) were observed. NR and bitumen are key components of TWP of bus tire tread and asphalt pavement wear particle (APWP), respectively. The TWP contents in the PM2.5 samples collected at the bus stop were larger than those collected at the college campus. For the same sampling site, the TWP content in the PM2.5 sample collected for higher fine dust concentration in the air was greater than that for lower one. The TWP2.5 concentration in the air for the BS sampling was higher than those for the CC sampling, even when the PM2.5 concentration in the air for the former was lower than those for the latter. It can be concluded that the TWPs and APWPs in the PM2.5 samples collected at the college campus should be transferred mostly from the outside road.

On the Association between Fine Dust Concentrations from Sand Dunes and Environmental Factors in the Taklimakan Desert

Dust in sand dunes is an effective and important source of dust emission. The Taklimakan Desert (TD) is one of the main sources of global dust: the sand dunes account for approximately 85% of the total area of the TD. However, the dust concentration, emission characteristics, and physical factors of different parts of the sand dunes in the TD during the day and night, as well as dust and non-dust days, remain unclear. In this study, dust observations were collected over a 3 month period in the TD to investigate the physical processes by which dust moves across a surface and generates PM10 and PM2.5 from the top and bottom of sand dunes. The results showed that the daily average maximum concentrations of particulate matter with diameters below 2.5 and 10 µm (PM2.5 and PM10) in the dune during the observation period reach ~90 and ~190 µg·m−3, respectively. Dust emission generated in the saltation process (maximum emission of PM10 was 3–5 mg·m−2·s−1) in the TD dunes was larger than that in other areas and had a good correlation with u* (friction velocity), where u* = 0.4 m·s−1 was the threshold of sand dune dust emission. The wind speed at the top of dunes was larger than that at the bottom, which was conducive to the accumulation of PM10 at the top of the dune. Furthermore, the MLH (mixed layer height) decreased after sunset and the turbulence weakens, which was conducive to the retention of dust in the air. Moreover, the dust made the solar radiation at the top of the dune −15 W·m−2 (average) lower than at the bottom. These results provided a new understanding of dune emissions in the TD and could be used for dust modeling in regional and global models.

Influence of friction velocity and saltation intensity on the particle‐size distribution of dust (PM32) emitted from an agricultural soil

AbstractThe size distribution of dust emitted from soils determines particle attributes such as the enrichment in elements that are important for biogeochemical cycles, global climate and human health. There is an ongoing discussion about the size distribution of dust and its relationship with saltation and wind speed. In this work, we present our own results and discuss them in the context of other studies, emphasizing on dust emission from agricultural soils, which is relatively complex and less studied. The objective of this work was to evaluate the particle size distribution of the emitted dust or particulate matter (PM) up to 32 μm in diameter (PM32) from an agricultural sandy soil under two saltation‐supply conditions, using a wind tunnel coupled to a dust‐suspension chamber. We found strong correlations between friction velocity, saltation flux and dust emission. Additional saltation supply increased the total dust PM32 emission at high wind speeds up to three‐fold. Correlation between wind speed and dust concentration was best explained by a power law, with exponents up to 12.04 for the 20 to 32 μm particle‐size range under high‐saltation conditions, but much lower (up to 4.34) for the 0.25 to 1 μm size range under low saltation. An increase of the wind speed affected significantly the concentration of the coarser dust fraction (PM &gt; 10 μm), but affected less the concentration of fine dust (PM from 10 to 1 μm) and did not affect the concentration of the ultrafine (PM &lt; 1 μm) fraction in the suspension chamber. Results found suggest that dust is mostly produced from particles less than 60 μm in diameter, both by fragmentation and direct wind lift. Results found herein reinforce one of the existent theories, but raise questions about the influence of the soil type, and the place and timing of the sampling arrangements.

Preliminary Results of Cloud Seeding Experiments for Air Pollution Reduction in 2020

In previous studies, cloud seeding has been primarily used to increase precipitation; however, its use for the purpose of reducing particulate matter concentration has not yet been adequately addressed. In this study, we investigated the effect of cloud seeding on the reduction of fine dust concentration by conducting two airborne experiments in west Korea along the Yellow Sea coast on November 1, 2020. Numerical simulations showed that the seeding material diffused in Seoul, Gyeonggi-do, and Chungcheongnam-do as observed from the prevailing wind direction. We also observed enhanced radar reflectivity in these areas, and analysis of aircraft observation data revealed that the average concentration of cloud, drizzle, and precipitation particles increased after seeding as compared with the observations before and during seeding. Further, the precipitation particles had large diameters after seeding; particularly, PM10 (particulate matter with size ≤ 10 µm) concentration tended to decrease owing to precipitation in the areas affected by cloud seeding; however, it remained unchanged in the unaffected areas. Although it is necessary to conduct further experiments to obtain more comprehensive results, the findings of this study highlight the possibility of reducing fine dust concentration in the atmosphere via cloud seeding.

COMPARATIVE ASSESSMENT OF SANITARY AND HYGIENIC NORMS OF HOUSING PIGS ACCORDING TO DIFFERENT PARATYPICAL FACTORS

Introduction. The relevance of the work is determined by the implementation of the method of multi-parametric assessment of the microclimate of premises for pigs by the method of continuous automatic registration. The purpose o f the research was to conduct a comparative assessment of indoor pollution with carbon dioxide, ammonia, hydrogen sulfide, methane, and fine dust based on various paratypical factors. Research methods. Analytical, zootechnical, physical, ethological and biometric research methods are applied. Research results. A comparative assessment of the sanitary and hygienic norms for housing pigs in brick premises on concrete and slotted floors in machines was carried out. It was determined that during the periods of the year, the average daily CO2 and NH3 air pollution in pig houses with concrete and slotted floors in the machines were close to the normative values of MPC. The volume concentration of methane was 1.9-3.4 times higher in the premise with a slotted floor in the machines, which is associated with the features of the technology of the self-alloying system of manure removal. According to the calculations of the correlation between the temperature and the relative humidity of the air outside and inside the premises, a probable mostly positive connection was determined. The locomotor activity indices of young pigs were 9.6-12.4 % higher on the slatted floor, but they were inferior in the food activity index by 6.4-16.2 % to peers kept on the concrete floor. The variance analysis determined the probable influence fi=0.784) of the method of housing pigs on solid concrete and slotted floors in machines on the variability of the volume concentration of methane. It was also found out that the period of the year likely influenced the variability of air temperature, volume concentration of carbon dioxide, ammonia and mass concentration of fine dust in the room fi= 0.799; 0.794; 0.352 and 0.439, respectively). Conclusions. The average daily volume concentration in the air of carbon dioxide and ammonia in pig houses by periods of the year approached the normative values of the MPC. The volume concentration of methane was higher in the premise with a slotted floor, which is related to the features of the technology of the self-alloying manure removal system. The probable influence of the method of housing pigs on the variability of indicators of relative air humidity and volume concentration of methane wasdetermined. The probable influence of the period of the year on the variability of air temperature indicators, volume concentration of carbon dioxide, ammonia and mass concentration of fine dust has also been proven. Key words: measuring and computing complex, microclimate parameters, comparative assessment, premise for pigs, paratypic factor

Analysis of Air Supply Air Flow Rate Decrease by Accelerated Dust Load Experiments on Heat-Recovery Ventilator Air Filter

Purpose: Recently, owing to the high concentration of fine dust, air filters have become essential in heat-recovery ventilators. In Korea, air filter performance is evaluated by the standard, “KS B 6141 Air Filter Units for Ventilation.” However, filter lifetime has not been examined. In this study, an accelerated test method for air filters was developed. In addition, the change in the supply air flow rate was analyzed under filter conditions. Method: The filter accelerated test conditions were identified, and the annual average concentration of fine dust in the atmosphere was determined. A vertical wind tunnel was built to evaluate the air filter lifetime. The ISO A2 dust was dosed to an air filter. In addition, the supply air flow rates were measured for the three products that the accelerated air filter used. Result: Under the lab test conditions, the supply air flow rate changed with the filter usage time and the air resistance. In the experiments, only ISO A2 dust was used. Subsequently, the effects of various particles were tested on the development of the acceleration test method for filters.

Numerical modeling of open pit aeration under major blasts according to the dust factor

Research objective is to estimate the effect of major blast location, dust and gas cloud initial height, the wind current velocity at the upper pit slope on the time of pit aeration and on the level of atmospheric pollution of the upper pit slope downwind according to the dust factor. Methods of research. Aerodynamics and fine dust transport in 2D geometry was modeled in the COMSOL program. The aerodynamic characteristics were calculated using the incompressible fluid approximation with the standard (k–ε) turbulence model. The process of fine dust spreading is modeled by numerically calculating the convective-diffusion equation for pollutants transfer, considering the settling rate. Numerical experiments were performed under a fixed initial concentration of fine dust and varying velocity of the oncoming wind current for three locations of major blasts and six values of the dust and gas cloud initial height (from 70 to 420 m with a step of 70 m). Results and analysis. Significant qualitative (but not quantitative) similarity was noted in the calculated spatial-temporal parameters for fine dust and the gas component. It is shown that for the recirculation aeration scheme, the longest duration of pit cleaning from dust is observed during major blasts displaced to the weather pit slope. A wave-like removal is predicted of dust pollutants with different heights of peaks and time to reach the peak value. Differences are demonstrated in the processes of pit aeration during major blasts in terms of fine dust and gas component, which do not contradict the physics of the process. Conclusions and scope. A functional dependence has been obtained, which makes it possible to predict the time of reaching the first peak of dust pollution at the upper pit slope downstream as a function of two variables, namely, the initial height of the dust and gas cloud rise and the oncoming wind current velocity.

A Machine Learning-Based Energy Management Agent for Fine Dust Concentration Control in Railway Stations

This study developed a reinforcement learning-based energy management agent that controls the fine dust concentration by controlling facilities such as blowers and air conditioners to efficiently manage the fine dust concentration in the station. To this end, we formulated an optimization problem based on the Markov decision-making process and developed a model for predicting the concentration of fine dust in the station by training an artificial neural network (ANN) based on supervised learning to develop the transfer function. In addition to the prediction model, the optimal policy for controlling the blower and air conditioner according to the current state was obtained based on the ANN to which the Deep Q-Network (DQN) algorithm was applied. In the case study, it is confirmed that the ANN and DQN of the predictive model were trained based on the actual data of Nam-Gwangju Station to converge to the optimal policy. The comparison between the proposed method and conventional method shows that the proposed method can use less power consumption but achieved better performance on reducing fine dust concentration than the conventional method. In addition, by increasing the value of the ratio that represents the compensation due to the fine dust reduction, the learned agent achieved more reduction on the fine dust concentration by increasing the power consumption of the blower and air conditioner.

공조설비와 에너지저장장치 제어를 위한 강화학습 기반 에너지관리 에이전트 개발

In this paper, we propose an energy management agent that controls HVAC facilities and ESS by using the Policy Gradient Method, which is one of the reinforcement learning techniques. For this purpose, based on supervised learning, an artificial neural network was constructed to predict the change in the concentration of fine dust in stations according to the control of fine dust reduction facilities. This was used as a transfer function of the Markov decision process, and the optimal policy based on the normal distribution expressed as conditional probability was obtained through the policy gradient method. In the case study, using the actual data of Nam-Gwangju Station, learning of the energy management agent based on artificial neural network and policy gradient method was conducted. It was confirmed that the total electricity cost was reduced by adjusting the charging and discharging of the energy storage device according to the electricity price by time period.

The Long-term Effect of Fine Dust Concentration on Exacerbation of Environmental Diseases for Vulnerable and Susceptible Individuals in South Korea : The Necessity of Personalized Standards on Air Quality Index

The Long-term Effect of Fine Dust Concentration on Exacerbation of Environmental Diseases for Vulnerable and Susceptible Individuals in South Korea : The Necessity of Personalized Standards on Air Quality Index

효율적인 국민DR 대상 세대 선정을 위한 전력사용량 군집화 및 부하 형태 분석

As Demand Response (DR) programs have been widely accepted to reduce energy consumption in residential buildings, targeting appropriate customers for demand response programs is crucial to minimize losses caused by the enrollment of inappropriate customers. Previous studies have mainly used the clustering method to group and analyze usage patterns of load profiles for demand response targeting. Although the national DR program reduction request is issued according to the concentration of fine dust, selecting households with high electricity consumption for the DR program can show effective results in reducing the power load. In this paper, we proposed a two-stage clustering method to segment households’ load profiles using a machine learning-based clustering algorithm. Then, the load parameter method was used to analyze the load shape for national demand response targeting. Our findings indicate that households with high morning peak and evening peak are suitable to be targets for the national DR program.

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.