Dust Period Research Articles

A study of the impact of entrance space on indoor air quality in vernacular dwellings in desert areas during sandstorms

Alxa League, located in the western part of Inner Mongolia, China, frequently suffers from sandstorms. Such dusty conditions result in sharp increases in respirable particulate matter (PM10) concentration in outdoor and indoor environments, posing a significant health risk to local inhabitants. Vernacular dwellings in this area feature a specific floor plan that includes an entrance space to safeguard indoor air quality during sandstorms while being compatible with the local climate. This study utilises CONTAM, a multizone indoor air quality and ventilation analysis computer program, and field measurements to quantitatively evaluate the effectiveness of the entrance space in protecting indoor air quality against ambient PM10 pollution under both dusty and normal conditions. The simulation results reveal that the entrance space can effectively mitigate PM10 pollution in the middle room, lowering the average concentration from 47.0 μg/m3 to 37.5 μg/m3 during dust periods. However, PM10 pollution may increase in the entrance space, reaching an average concentration of up to 70.0 μg/m3. Experimental outcomes align with the simulated results. Given that construction activities exacerbate desertification and frequent sandstorms, the above findings help identify the optimal design strategies for energy-efficient green vernacular dwellings in the targeted desert area, promoting environmental harmony and addressing climate change challenges.

Comparison of the polluted dust plume and natural dust air mass in a spring dust event in Beijing 2023

The occurrence of extreme weather events is becoming more frequent due to global climate change. A long-lasting dust outbreak in the spring of 2023 was triggered by Mongolia cyclones and cold fronts in the dust source areas. In this study, we illustrate the spatial distribution, the transport path of the dust and its influence on the air quality of downstream cities utilizing ground-based and space-borne measurements. Results indicate a more complicated pollution, coexisting of polluted dust stage S1 and pure dust stage S2. In S1, the aerosol was characterized by a dual-layer vertical structure—high extinction coefficient of nearly 1.0 km−1 with a low particle depolarized ratio (PDR) of < 0.1 under 500 m, and a small extinction coefficient of 0.3 km−1 with a high PDR of 0.15 above 500 m. Then the intrusion of the Mongolian cyclone carried new dust plumes on 10 March, suggesting the onset of pure dust period S2. The source transition was also confirmed by the MODIS, CALIPSO and Hysplit observations. The pure Asian dust evolved rapidly with one thickening layer and distributed homogeneously in the boundary layer. The PDR increased significantly to the peak of 0.35 and resulted in the peak PM10 value of > 1300 µg/m3. PM10 positively correlated with trace gases in S1 while varying inversely with the pollution gases in S2. The results help to shed light on the classification of different types of dust and also be useful in developing an effective strategy to forecast air pollution in downstream areas.

Aerosol soluble proteins in Asian dust in southwestern Japan

Aerosol proteins, as core biological components of bioaerosols, are garnering increasing attention due to their environmental significance, including their roles in atmospheric processes and associated health risks. However, observational data on the proteins are very limited, leaving their distribution and variation in the atmosphere poorly understood. To investigate the long-distance transport of proteins with Asian dust in the Northern Hemisphere middle latitude westerlies to remote downwind areas, we quantified the soluble proteins in aerosol particles, referred to as aerosol soluble proteins (ASPs), collected in the coastal city of Kumamoto, Japan, during the spring of 2023, when three dust events occurred. The concentration of ASPs ranged from 0.22 to 1.68 μg m−3, with an average concentration of 0.73 ± 0.36 μg m−3 under dust conditions and 0.31 ± 0.05 μg m−3 under non-dust conditions. During the dust periods, the largest concentration of ASPs (1.68 μg m−3) coincided with the peak concentration of suspended particulate matter, and the concentration strongly correlated with the mass concentration of particles larger than 2.5 μm, indicating a close dependence of ASPs on dust particles. Primary estimations indicated a dry deposition flux of ASPs at approximately 1.10 ± 0.87 mg m−2 d−1 under the dust conditions. These results prove that Asian dust efficiently transports proteins, facilitating their dispersion in the atmosphere.

The Lower Atmospheric Characteristics of Dust Storms Using Ground-Based Sensor Data: A Comparative Analysis of Two Cases in Jinan, China

Two severe dust storm (DS) events (15–17 March and 28–29 March) hit northern China in 2021 consecutively. The lower atmospheric vertical dynamic and thermal structures during the two cases were compared using the ground-based sensor data from the microwave radiometer and radar wind profiler, combined with the environmental and meteorological observations data in Jinan, China. It was found that both cases occurred under the background of cold vortexes over northeastern China. The dust was transported through the cold air on the northwest route. During the dust period, 2–3 km was the west or northwest airflow, and below 2 km was the northeast wind. The variation in the dynamic structure determined the duration of the DS. During the DS maintenance phase, the vertical wind shear (VWS) below 3 km measured approximately 10 m∙(s∙km)−1. The increased VWS during the dust intrusion period facilitated the transportation of dust. In contrast, the more significant VWS was not conducive to the maintenance of DS, and the shift to south wind control in the upper middle layer indicated the weakening of DS. In both cases, we observed a cliff-like decrease in relative humidity as a prominent indicator of dust outbreaks, occurring approximately 2–5 h beforehand. The diurnal difference between the vertical temperature and relative humidity during the dust maintenance period was found to be insignificant.

Improved Air Quality Forecasting-Based on Machine Learning and Multivariate Regression Techniques

Hourly-based improved prediction of present air quality state (PM10, PM2.5, and NO2) with 15 input independent variables (3 hours\' earlier PM, gas and meteorological data of a Korean city affected by 2 days\' earlier PM and gas data of Beijing city in the Yellow Dust route was performed using Machine Learning (ANN-Tanh) and Multivariate Regression techniques. ANN-tanh model of multilayer perception (MLP) structure with a feed-forward input data and backpropagation training process for error calculation with 15 nodes in a single hidden layer was adopted to calculate present urban air quality. Root mean square error (RMSE) and the coefficient of determination (R2; Pearson R) evaluated the performance ability of the model between the predicted and measured values before, during, and after the Yellow Sand event at the Korean coastal city. Multivariate regression technique was also used to predict the urban air quality with the same input data. The prediction accuracies of the two models were compared before, during, and after the Yellow Dust period and regardless of the event, Pearson R correlation coefficients using the ANN-tanh model (multivariate regression model) were quite high as 0.935 (0.961), 0.943 (0.948) and 0. 947 (0.920) for PM10, 0.942 (0.909), 0.969 (0.977) and 0.938 (0.947) for PM2.5, and 0.925 (0.860), 0.853 (0.875) and 0.886 (0.903) for NO2, respectively. Their prediction ability overall was quite excellent, showing a slightly higher R in the ANN-Tanh. Their hourly distributions of predicted and measured values of 3 outputs were drawn to compare their model performance.

Intrinsic and extrinsic techniques for quantification uncertainty of an interpretable GRU deep learning model used to predict atmospheric total suspended particulates (TSP) in Zabol, Iran during the dusty period of 120-days wind

Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP concentrations are necessary to mitigate their negative effects. This study applies the gated recurrent unit (GRU) deep learning model to predict TSP concentrations in Zabol, Iran, during the dust period of the 120-day wind (3 June - 4 October 2014). Three uncertainty quantification (UQ) techniques consisting of the blackbox metamodel, heteroscedastic regression and infinitesimal jackknife were applied to quantify the uncertainty associated with GRU model. Permutation feature importance measure (PFIM), based on the game theory, was employed for the interpretability of the predictive model's outputs. A total of 80 TSP samples were collected and were randomly divided as training (70%) and validation (30%) datasets, while eight variables were used in the TSP prediction model. Our findings showed that GRU performed very well for TSP prediction (with r and Nash Sutcliffe coefficient (NSC) values above 0.99 for both datasets, and RMSE of 57 μg m−3 and 73 μg m−3 for training and validation datasets, respectively). Among the three UQ techniques, the infinitesimal jackknife was the most accurate one, while all the observed and predicted TSP values fell within the continence limitation estimated by the model. PFIM plots showed that wind speed and air humidity were the most and least important variables, respectively, impacting the predictive model's outputs. This is the first attempt of using an interpretable DL model for TSP prediction modelling, recommending that future research should involve aspects of uncertainty and interpretability of the predictive models. Overall, UQ and interpretability techniques have a key role in reducing the impact of uncertainties during optimization and decision making, resulting in better understanding of sophisticated mechanisms related to the predictive model.

Aerosol-CCN characteristics and dynamics associated with a pre-monsoon dust storm over a high-altitude site in Western Ghats, India.

Aerosol-CCN characteristics and dynamics during a pre-monsoon dust storm (April 6-11, 2015) over a high-altitude site ((17.92°N, 73.66°E, and 1348m above mean sea level (MSL)) in Western Ghats, India, has been studied using ground-based observations, satellite, and reanalysis datasets. Spatial distribution of dust surface mass concentration along with the back trajectory analysis showed the Arabian Desert area (Rub-Al-khali desert) as the source region and strong westerly winds transported the dust particles toward the Indian subcontinent. High values noticed in the surface PM10 (PM2.5), i.e., ~ 450 (~ 130) µg m-3, MODIS AOD550nm (0.6), and MERRA 2 dust surface mass concentration (5 × 10-7kgm-3) along MODIS true color images confirmed the dust storm event on April 6, 2015 over the observational site. Size-segregated aerosol number concentration measured from ground-based observations showed the dominance of Aitken, accumulation, and coarse mode particles during dust period. CCN concentrations at 0.1, 0.3, 0.5, 0.7, and 0.9% SS were analyzed. A low value of CCN concentration and activation fraction (~ 0.3) near surface was noticed during dust storm day, suggesting insoluble mineral dust particle being transported. Analyzed vertical velocity during pre-dust period showed downdraft between 900 and 750hPa, suggesting dust transport from upper altitudes toward the observational site. WRF-Chem model simulation also captured the dust storm event, and the results are in good agreement with the observation with a significance of 95% confidence level.

Spatiotemporal characteristics and driving mechanisms of PM10 in arid and semi-arid cities of northwest China

PM10 pollution is a pressing ecological concern within arid and semi-arid cities, adversely influencing the natural environment and human health. Based on the Geographically and Temporally Weighted Regression (GTWR) model, this study comparatively investigated the spatiotemporal dynamics of PM10 during non-dust and dust periods in five arid cities (Jiuquan, Jiayuguan, Zhangye, Jinchang, and Wuwei) and one semi-arid city (Lanzhou) in northwest China. Further, this study examined the critical mechanisms of PM10 via the GTWR and Geographic Detector model. A comparative analysis illustrated that dust period PM10 concentrations surpassed non-dust period concentrations. The annual average PM10 concentrations during the non-dust period wavily declined from 2015 to 2020, exhibiting significantly higher levels in the semi-arid city as opposed to arid cities. Conversely, during the dust period, PM10 levels rose initially between 2015 and 2017 and diminished from 2018 to 2020, with high concentration clusterings primarily observed in 2015–2016 in western arid cities, Jiuquan and Jiayuguan, in 2017–2019 in eastern cities, Wuwei and Lanzhou, and in 2020 in central arid cities, Jinchang and Zhangye. During the non-dust period from 2015 to 2020, the cumulative effects of single driving factors were ranked in the following order: soil moisture content (MOI), geographical location (LOC), precipitation (PRE), SO2, gross domestic product (GDP), wind speed (WS), and digital elevation (DEM) while during the dust period, the emphasized factors were ranked in the following order: MOI, LOC, PRE, WS, SO2, GDP, and DEM. The interaction between MOI and WS on PM10 was most noticeable in this study (non-dust: 0.787, dust: 0.832). Thus, MOI and WS are critical components in controlling PM10 pollution. The outcomes of this study can furnish theoretical foundations for ecological construction in Northwest China, unlock novel insights for ecological measures in other global arid and semi-arid cities.

Machine learning-assisted dispersion modelling based on genetic algorithm-driven ensembles: An application for road dust in Helsinki

A novel Machine Learning (ML) approach is proposed to assist the dispersion modelling of road dust in an urban area. The aim is to improve ENFUSER model's coarse particle nowcasting estimations, especially during the road dust period in springtime. We assist the dispersion model by combining road condition measurements, regional-scale air quality forecasts, meteorological data, road maintenance information, detailed traffic patterns, as well as GIS-data, for the Helsinki area, via a ML based data fusion procedure. The proposed pipeline consists of feature engineering, a set of feature selection methods, and a Genetic Algorithm search for the optimal stacking ensemble configuration, as well as robust validation in both space and time dimensions. Results suggest that our approach enhances the current ENFUSER capabilities, and thus could be applied to assist its performance under operational conditions, while also offering potential as a versatile tool for addressing a broad range of similar tasks.

Seasonal variations, source apportionment and dry deposition of chemical species of total suspended particulate in Pengjia Yu Island, East China Sea

Total of 172 total suspended particulate (TSP) samples and its chemical compositions were collected and analyzed from January to December 2010 in Pengjia Yu Island, an open region in East China Sea (ECS). Despite the predominance of sea-salt major ions (Na+, Cl−), the presence of non-sea-salt SO42− (nss-SO42−) and NO3− as well as combustion-derived trace metals clearly establishes the impact of anthropogenic sources over ECS. The annual contributions of coal, heavy-fuel oil and traffic to the measured chemical species were 21.0 %, 15.0 % and 15.5 %, respectively. Especially in spring, the contributions of crustal minerals to measured chemical species during dust period (33.6 %) were higher than that (13.2 %) during non-dust period. The calculated annual average dry deposition fluxes for trace metals and total inorganic nitrogen were 246.1 ± 345.8 μg/m2/d and 2950.4 ± 2245.0 μg/m2/d, suggesting that atmospheric deposition is an important source of nutrient elements for the south of ECS.

Abrupt High PM Concentration in an Urban Calm Cavity Generated by Internal Gravity Waves and a Shallow Coastal Atmospheric Boundary Layer with the Influence of the Yellow Dust from China

Using GRIMM-1107 aerosol sampler, GOES-9 DCD satellite images, HYSPLIT model of backward trajectory and 3D-meteorological WRF-3.6 model, high particulate matter concentrations were investigated at Gangneung city in the Korean east coast which consists of Mt. Taeglyung in its west and the East Sea in its east on 00:00LST March 26~00:00 LST 4 April 2004. During a Yellow Dust period, the maximum PM10 (PM2.5 and PM1) concentration at the city was about 3.3 (1.1 and 1.01) times higher than one in the non-dust period. After the transported dust from the Gobi Desert and Nei-Mongo by strong northwesterly wind passed over Mt. Taegulyang and moved down toward the city. Then the dust was trapped inside a calm cavity generated by the confront of internal gravity waves (IGW) over the city and the eastward movement of the trapped dust is prohibited by the easterly onshore wind from the East Sea, and the trapped dust further combined with particulate and gaseous emitted from the road vehicles and heating boilers of the city at 09:00LST, March 30 (beginning time of office hours), causing high PM concentrations. On mid-day, as the combined dust due to daytime sufficient thermal convection rises up to the top of the thermal internal boundary layer (TIBL) of a 300 m depth from the coast to the top of the mountain, the ground-based PM concentrations in the city are much lower at 15:00LST due to the higher thickness of the TIBL than at 09:00LST. At night, particulates emitted from many road vehicles after the end of office hours and residential heating boilers could combine with both dust transported from the Nei-Mongo and falling dust uplifted from the ground surface of the city during the day, and they were trapped inside a calm cavity by the IGV under much shallower stable nocturnal surface inversion layer than the TIBL, causing more dust to be accumulated near the surface and showing the maximum PM concentrations at 20:00 LST.

Impacts of dust events on chemical characterization and associated source contributions of atmospheric particulate matter in northern China

Sand and dust have significant impacts on air quality, climate, and human health. To investigate the influences of dust storms on chemical characterization and source contributions of fine particulate matter (PM2.5) in areas with different distances from dust source regions, PM2.5 and associated chemical composition were measured in two industrial cities with one near sand sources (i.e., Wuhai) and the other far from sand sources (i.e., Jinan) in northern China in March 2021. Results showed that PM mass concentrations significantly increased and exceeded the Chinese National Ambient Air Quality standard during the dust events, with absolute concentrations and fractional contributions of PM2.5-bound crustal and trace elements increased while secondary inorganic ions decreased at both sites. Crustal materials dominated the increased PM2.5 mass from non-dust period to dust period in both cities. These were further evidenced by PM2.5 source apportionment results from positive matrix factorization model. During the dust events, dust sources contributed up to 88% of PM2.5 mass in Wuhai and ∼38% of PM2.5 mass in Jinan, a city about thousands of kilometers away from the sand source. Besides, the measurement data indicated that dust from northwest China may also bring along with high abundance of organic matter and vanadium. Secondary and traffic sources were two of the most important source contributors to PM2.5 in both cities during the non-dust periods. However, the near sand source city was more susceptible to the aggravating effects of dust and minerals, with much higher contributions by crustal materials (∼47%, from the aspect of chemical components) and dust-related sources (∼26%, from the aspect of sources) to PM2.5 mass even during non-dust periods. This study highlighted the urgent need for more action and effective control of sand sources to reduce the impact on air quality in downstream regions.

Investigation of a haze-to-dust and dust swing process at a coastal city in northern China part I: Chemical composition and contributions of anthropogenic and natural sources

The long retention of dust air masses in polluted areas, especially in winter, may efficiently change the physicochemical properties of aerosols, causing additional health and ecological effects. A large-scale haze-to-dust weather event occurred in the North China Plain (NCP) region during the autumn-to-winter transition period in 2018, affecting the coastal city Qingdao several times between Nov. 27th and Dec. 1st. To study the evolution of the pollution process, we analyzed the chemical characteristics of PM2.5 and PM10–2.5 and source apportionments of PM2.5 and PM10, The dust stagnated around NCP and moved out and back to the site, noted as dust swing process, promoting SO42− formation in PM2.5 and NO3− formation in PM10–2.5. Source apportionments were analyzed using the Positive Matrix Factorization (PMF) receptor model and weighted potential source contribution function (WPSCF). Before the dust invasion, Qingdao was influenced by severe haze; waste incineration and coal burning were the major contributors (~80 %) to PM2.5, and the source region was in the southwest of Shandong Province. During the initial dust event, mineral dust and the mixed factor of dust and sea salt were the major contributors (46.0 % of PM2.5 and 86.5 % of PM10). During the polluted dust period, the contributions of regional transported biomass burning (22.3 %), vehicle emissions (20.8 %), and secondary aerosols (33.8 %) to PM2.5 from the Beijing–Tianjin–Hebei region significantly increased. The secondary aerosols source was more regional than that of vehicle emissions and biomass burning and contributed considerably to PM10 (30.8 %) during the dust swing process. Our findings demonstrate that environmental managers should consider the possible adverse effects of winter dust on regional and local pollution.

A Case Analysis of Dust Weather and Prediction of PM10 Concentration Based on Machine Learning at the Tibetan Plateau

Dust weather is common and disastrous at the Tibetan Plateau. This study selected a typical case of dust weather and analyzed its main development mechanism in the northeast of the Tibetan Plateau, then applied six machine learning methods and a time series regression model to predict PM10 concentration in this area. The results showed that: (1) The 24-h pressure change was positive when the front intruded on the surface; convergence of vector winds with a sudden drop in temperature and humidity led by a trough on 700 hPa; a “two troughs and one ridge” weather situation appeared on 500 hPa while the cold advection behind the trough was strong and a cyclone vorticity was formed in the east of Inner Mongolia. (2) The trajectory of air mass from the Hexi Corridor was the main air mass path influencing Xining City, in this case, since a significant lag in the peak of PM10 concentration appeared in Xining City when compared with Zhangye City. (3) The Multiple Linear Regression was not only timely and effective in predicting the PM10 concentration but had great abilities for anticipating the transition period of particle concentration and the appearance date of maximum values in such dust weather. (4) The MA and MP in the clean period were much lower than that in the dust period; the PM10 of Zhangye City as an eigenvalue played an important role in predicting the PM10 of Xining City even in clean periods. Different from dust periods, the prediction effect of Random Forest Optimized by Bayesian hyperparameter was superior to Multiple Linear Regression in clean periods.

Oxidative potential of size-segregated particulate matter in the dust-storm impacted Hotan, northwest China

Reactive oxygen species (ROS) induced by particulate matter (PM), of which •OH is the most active free radical, have adverse health effects; however, PM-induced generation of ROS and •OH has not been quantified and studied in northwest China, which suffers from severe dust storms. In this study, ambient PM10 and PM2.5 collected from the urban area of Hotan in the Uygur Autonomous Region of China, during dust and non-sand periods in 2020. ROS and •OH were quantified and examined using the dichlorofluorescein diacetate (DCFH) assay and fluorescent probe method. The results showed that the oxidation potential (OP) of PM in the non-dust period (PM2.5: 7.77 ± 0.29 nmol H2O2 m−3; PM10: 8.9 ± 0.38 nmol H2O2 m−3) was higher than in the dust period (PM2.5: 1.14 ± 0.71 nmol H2O2 m−3; PM10: 1.85 ± 0.77 nmol H2O2 m−3). Compared to PM10, PM2.5 with larger specific surface area is more likely to adsorb the components contributing to ROS generation, so the induction amount of ROS and •OH per unit volume in the environment will increase with increasing PM2.5 concentration. There was a weak correlation between O3 and ROS concentrations in the PM during the day, suggesting that photochemical reactions were a moderating factor in the formation of daytime ROS. In PM2.5, ROS showed a weak correlation with Pb, Cu, and Fe during the dust period. During the non-dust period, significantly correlated with Fe, Cu, and Cd (P < 0.01; P < 0.05). In PM10, ROS correlated significantly with As (P < 0.05) but weakly with Fe and Pb during the dust period. During the non-dust period, significantly correlated with As and Cd (P < 0.01; P < 0.05) and weakly correlated with Ni, Cu, and Fe. This indicates that increasing trace metal levels can increase the OP of atmospheric PM.

Analyses of a Lake Dust Source in the Middle East through Models Performance

Drying lakes have become a new source of dust, causing severe problems in surrounding areas. From 2000 to 2017, a statistical study was conducted on Lake Urmia in Iran in the Middle East. The results indicated a significant increase in the annual number of dusty days in stations around the lake and the mean annual aerosol optical depth (AOD) at 550 nm. The sharp decrease in annual snowfall rate over the Lake Urmia area since 2007 has been linked to the lake’s decreasing water level and drying. During a dust storm event from 27 October to 31 October 2017, a local dust storm originated from Lake Urmia before another large-scale dust storm originated from the An-Nafud desert. According to MODIS true-color images, dust particles were lifted from Lake Urmia and transported eastward to the Caspian Sea and the HYSPLIT model. The comparison of the four models under the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) revealed that the models overestimated surface dust concentrations compared to ground-based PM10 measurements. Nevertheless, the NOAA/WRF-Chem and DREAMABOL models simulated higher dust concentrations during the dust period. More emphasis should be placed on the development of dust models for SDS-WAS models in Lake Urmia.

Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification

Abstract. The Cape Verde Atmospheric Observatory (CVAO), which is influenced by both marine and desert dust air masses, has been used for long-term measurements of different properties of the atmospheric aerosol from 2008 to 2017. These properties include particle number size distributions (PNSD), light-absorbing carbon (LAC) and concentrations of cloud condensation nuclei (CCN) together with their hygroscopicity. Here we summarize the results obtained for these properties and use an unsupervised machine learning algorithm for the classification of aerosol types. Five types of aerosols, i.e., marine, freshly formed, mixture, moderate dust and heavy dust, were classified. Air masses during marine periods are from the Atlantic Ocean and during dust periods are from the Sahara Desert. Heavy dust was more frequently present during wintertime, whereas the clean marine periods were more frequently present during springtime. It was observed that during the dust periods CCN number concentrations at a supersaturation of 0.30 % were roughly 2.5 times higher than during marine periods, but the hygroscopicity (κ) of particles in the size range from ∼ 30 to ∼ 175 nm during marine and dust periods were comparable. The long-term data presented here, together with the aerosol classification, can be used as a basis to improve our understanding of annual cycles of the atmospheric aerosol in the eastern tropical Atlantic Ocean and on aerosol-cloud interactions and it can be used as a basis for driving, evaluating and constraining atmospheric model simulations.

Enhanced secondary organic aerosol formation during dust episodes by photochemical reactions in the winter in Wuhan

To investigate the effect of frequently occurring mineral dust on the formation of secondary organic aerosol (SOA), 106 volatile organic compounds (VOCs), trace gas pollutants and chemical components of PM2.5 were measured continuously in January 2021 in Wuhan, Central China. The observation period was divided into two stages that included a haze period and a following dust period, based on the ratio of PM2.5 and PM10 concentrations. The average ratio of secondary organic carbon (SOC) to elemental carbon (EC) was 1.98 during the dust period, which was higher than that during the haze period (0.69). The contribution of SOA to PM2.5 also increased from 2.75% to 8.64%. The analysis of the relationships between the SOA and relative humidity (RH) and the odd oxygen (e.g., OX = O3 + NO2) levels suggested that photochemical reactions played a more important role in the enhancement of SOA production during the dust period than the aqueous-phase reactions. The heterogeneous photochemical production of OH radicals in the presence of metal oxides during the dust period was believed to be enhanced. Meanwhile, the ratios of trans-2-butene to cis-2-butene and m-/p-xylene to ethylbenzene (X/E) dropped significantly, confirming that stronger photochemical reactions occurred and SOA precursors formed efficiently. These results verified the laboratory findings that metal oxides in mineral dust could catalyse the oxidation of VOCs and induce higher SOA production.

Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods

Multiple statistical prediction modeling of PM10, PM2.5 and PM1 at Gangneung city, Korea, was performed in association with local meteorological parameters (air temperature, wind speed and relative humidity) and PM10 and PM2.5 concentrations of an upwind site in Beijing, China, in the transport route of Chinese yellow dusts which originated from the Gobi Desert and passed through Beijing to the city from 18 March to 27 March 2015. Before and after the dust periods, the PM10, PM2.5 and PM1 concentrations showed as being very high at 09:00 LST (the morning rush hour) by the increasing emitted pollutants from vehicles and flying dust from the road and their maxima occurred at 20:00 to 22:00 LST (the evening departure time) from the additional pollutants from resident heating boilers. During the dust period, these peak trends were not found due to the persistent accumulation of dust in the city from the Gobi Desert through Beijing, China, as shown in real-time COMS-AI satellite images. Multiple correlation coefficients among PM10, PM2.5 and PM1 at Gangneung were in the range of 0.916 to 0.998. Multiple statistical models were devised to predict each PM concentration, and the significant levels through multi-regression analyses were p &lt; 0.001, showing all the coefficients to be significant. The observed and calculated PM concentrations were compared, and new linear regression models were sequentially suggested to reproduce the original observed PM values with improved correlation coefficients, to some extent.

Distribution and sources of PM2.5-bound free silica in the atmosphere of hyper-arid regions in Hotan, North-West China

The composition of atmospheric fine particulate matter (PM2.5) is complex and exhibits strong regional differences. Free silica (α-SiO2) in atmospheric particulate matter is carcinogenic and is an important component of respirable particulate matter in urban areas. Measurements determined that the concentration of silicon dioxide (α-SiO2) in PM2.5 in the urban area of Hotan City, China, was 8.02 μg·m−3 during the dust period and exceeded 1.77 μg·m−3 during the non-dust period. The proportion of α-SiO2 in PM2.5 was 8.07% during the dust period and 2.25% during the non-dust period. Atmospheric visibility during the dust period was mainly influenced by the content of atmospheric floating dust. Analysis of α-SiO2 pollution sources during the dust period showed that the air masses containing sand and dust originated from the desert hinterland. Following passage through oasis areas, the air mass was effectively reduced in the concentration of α-SiO2 in PM2.5. During the dusty period, α-SiO2 and PM2.5 originated from the same source in Hotan City. Moreover, wind speed was the main influencing factor for the α-SiO2 concentration. During the non-dust period, α-SiO2 and PM2.5 were not from the same source of pollution.