peer reviewed | It is now irrefutable that air pollution caused by large amounts of Total Suspended Particulates (TSP) and respiratory particulates or Particulate Matter less than 10 mu m in aerodynamic diameter (PM10) has numerous undesired consequences on human health. Air quality degradation far from the African continent, in the US and in Europe, caused by high concentrations of African dust, is seen as a major threat even though most of these countries are very distant from the Sahara. Surprisingly, no estimates of TSP or PM10 levels near the Saharan dust source are available. Based on horizontal visibility observations which are reduced by the presence of dust in the atmosphere, TSP and PM10 levels are estimated throughout the year 2000 at Nouakchott-Airport, Mauritania, using relations found in the literature. It appears that concentrations of particles are significant both in terms magnitude and frequency, as the 24-hour PM10 thresholds established by the US EPA National Ambient Air Quality Standards and the EU Limits Values for Air Quality were exceeded 86 and 137 times, respectively. The average annual concentration is far above air quality standards and estimated at 159 mu g m(-3) for TSP and 108 mu g m(-3) for PM10. These very high particulate levels are likely to represent an important public health hazard and should be considered as a major environmental risk.

Long-range transport (LRT) reportedly carries air pollutants and microorganisms to downwind areas. LRT can be of various types, such as dust storm (DS) and frontal pollution (FP); however, studies comparing their effects on bioaerosols are lacking. This study evaluated the effect of LRT on viral and bacterial concentrations in Northern Taiwan. When LRT occurred and possibly affected Taiwan from August 2013 to April 2014, air samples (before, during, and after LRT) were collected in Cape Fugui (CF, Taiwan’s northernmost point) and National Taiwan University (NTU). Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was applied to quantify influenza A virus. qPCR and qPCR coupled with propidium monoazide were, respectively, used to quantify total and viable bacteria. Types and occurrence of LRT were confirmed according to the changing patterns of meteorological factors and air pollution, air mass sources (HYSPLIT model), and satellite images. Two Asian DS and three FP cases were included in this study. Influenza A virus was detected only on days before and during FP occurred on January 3–5, 2014, with concentrations of 0.87 and 10.19 copies/m³, respectively. For bacteria, the increase in concentrations of total and viable cells during Asian DSs (17–19 and 25–29 November 2013) was found at CF only (from 3.13 to 3.40 and from 2.62 to 2.85 log copies/m³, respectively). However, bacterial levels at NTU and CF both increased during FP and lasted for 2 days after FP. In conclusion, LRT increased the levels of influenza A virus and bacteria in the ambient air of Northern Taiwan, particularly at CF. During and 2 days (at least) after LRT, people should avoid outdoor activities, especially in case of FP.

To control the spread of COVID-19, China implemented a series of lockdowns, limiting various offline interactions. This provided an opportunity to study the response of air quality to emissions control. By comparing the characteristics of pollution in the summers of 2019 and 2020, we found a significant decrease in gaseous pollutants in 2020. However, particle pollution in the summer of 2020 was more severe; PM₂.₅ levels increased from 35.8 to 44.7 μg m⁻³, and PM₁₀ increased from 51.4 to 69.0 μg m⁻³ from 2019 to 2020. The higher PM₁₀ was caused by two sandstorm events on May 11 and June 3, 2020, while the higher PM₂.₅ was the result of enhanced secondary formation processes indicated by the higher sulfate oxidation rate (SOR) and nitrate oxidation rate (NOR) in 2020. Higher SOR and NOR were attributed mainly to higher relative humidity and stronger oxidizing capacity. Analysis of PMₓ distribution showed that severe haze occurred when particles within Bin2 (size ranging 1–2.5 μm) dominated. SO₄²⁻₍₁/₂.₅₎ and SO₄²⁻₍₂.₅/₁₀₎ remained stable under different periods at 0.5 and 0.8, respectively, indicating that SO₄²⁻ existed mainly in smaller particles. Decreases in NO₃⁻₍₁/₂.₅₎ and increases in NO₃⁻₍₂.₅/₁₀₎ from clean to polluted conditions, similar to the variations in PMₓ distribution, suggest that NO₃⁻ played a role in the worsening of pollution. O₃ concentrations were higher in 2020 (108.6 μg m⁻³) than in 2019 (96.8 μg m⁻³). Marked decreases in fresh NO alleviated the titration of O₃. Furthermore, the oxidation reaction of NO₂ that produces NO₃⁻ was dominant over the photochemical reaction of NO₂ that produces O₃, making NO₂ less important for O₃ pollution. In comparison, a lower VOC/NOₓ ratio (less than 10) meant that Beijing is a VOC-limited area; this indicates that in order to alleviate O₃ pollution in Beijing, emissions of VOCs should be controlled.

The physico-chemical properties of dust particles collected During Dust Storm (DDS) and After Dust Storm (ADS) events were studied using Scanning Electron Microscope coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS), X-ray Fluorescence Spectroscopy (XRF) and X-ray Photoelectron Spectroscopy (XPS). Morphological and compositional change in dust particles were observed as they react with the anthropogenic pollutants present in the urban environment. The calcite rich particles were observed to transform into calcium chloride, calcium nitrate, and calcium sulfate on reacting with the chlorides, nitrates, and sulfates present in the urban atmosphere. The frequency distributions of Aspect Ratio (AR) for the DDS and ADS particles were observed to be bimodal (mode peaks at 1.2 and 1.5) and monomodal (mode peak at 1.1), respectively. The highly irregular shaped solid dust particles were observed to transform into nearly spherical semisolid particles in the urban environment. XPS analysis confirms the high concentration of oxides, nitrates, and chlorides at the surface of ADS samples which show the signatures of mineral dust particles aging. Species with a high value of imaginary part of refractive index (like Cr metal, Fe metal, Cr₂O₃, FeO, Fe₂O₃) were observed at the surface of dust particles. At 550 nm wavelength, the light-absorbing potential of the observed species along with black carbon (BC) was found to vary in the order; Cr metal > Fe metal > Cr₂O₃> FeO > BC > Fe₂O₃> FeOOH. The presence of the aforementioned species on the surface of ADS particles will tremendously affect the particle optical and radiative properties compared to that of DDS particles. The present work could reduce the uncertainty in the radiation budget estimations of mineral dust and assessment of their climatic impacts over Delhi.

This study investigated the effects of pollution emissions on the bioreactivity of PM2.5 during Asian dust periods. PM2.5 during the sampling period were 104.2 and 85.7 μg m−3 in Xi'an and Beijing, respectively, whereas PM2.5 which originated from the Tengger Desert was collected (dust background). Pollution conditions were classified as non-dust days, pollution episode (PE), dust storm (DS)-1, and DS-2 periods. We observed a significant decrease in cell viability and an increase in LDH that occurred in A549 cells after exposure to PM2.5 during a PE and DS-1 in Xi'an and Beijing compared to Tengger Desert PM2.5. Positive matrix factorization was used to identify pollution emission sources. PM2.5 from biomass and industrial sources contributed to alterations in cell viability and LDH in Xi'an, whereas vehicle emissions contributed to LDH in Beijing. OC, EC, Cl−, K+, Mg2+, Ca, Ti, Mn, Fe, Zn, and Pb were correlated with cell viability and LDH for industrial emissions in Xi'an during DS. OC, EC, SO42−, S, Ti, Mn, and Fe were correlated with LDH for vehicle emissions in Beijing during DS. In conclusion, the dust may carry pollutants on its surface to downwind areas, leading to increased risks of particle toxicity.

Continuous measurements of PM2.5, PM10 and CO were conducted at an urban site of Udaipur in India from April 2011 to March 2012. The annual mean concentrations of PM2.5, PM10 and CO were 42 ± 17 μg m−3, 114 ± 31 μg m−3 and 343 ± 136 ppbv, respectively. Concentrations of both particulate and CO showed high values during winter/pre-monsoon (dry) period and lowest in the monsoon season (wet). Local anthropogenic emission and long-range transport from open biomass burning sources along with favourable synoptic meteorology led to elevated levels of pollutants in the dry season. However, higher values of PM10/PM2.5 ratio during pre-monsoon season were caused by the episodes of dust storm. In the monsoon season, flow of cleaner air, rainfall and negligible emissions from biomass burning resulted in the lowest levels of pollutants. The concentrations of PM2.5, PM10 and CO showed highest values during morning and evening rush hours, while lowest in the afternoon hours. In winter season, reductions of PM2.5, CO and PM10 during weekends were highest of 15%, 13% and 9%, respectively. In each season, the highest PM2.5/PM10 ratio coincided with the highest concentrations of pollutants (CO and NOX) indicating predominant emissions from anthropogenic sources. Exceptionally high concentrations of PM10 during the episode of dust storm were due to transport from the Arabian Peninsula and Thar Desert. Up to ∼32% enhancements of PM10 were observed during strong dust storms. Relatively low levels of O3 and NOx during the storm periods indicate the role of heterogeneous removal.

Hourly PM₂.₅ speciation data have been widely used as an input of positive matrix factorization (PMF) model to apportion PM₂.₅ components to specific source-related factors. However, the influence of constant source profile presumption during the observation period is less investigated. In the current work, hourly concentrations of PM₂.₅ water-soluble inorganic ions, bulk organic and elemental carbon, and elements were obtained at an urban site in Nanjing, China from 2017 to 2020. PMF analysis based on observation data during specific pollution (firework combustion, sandstorm, and winter haze) and emission-reduction (COVID-19 pandemic) periods was compared with that using the whole 4-year data set (PMFwₕₒₗₑ). Due to the lack of data variability, event-based PMF solutions did not separate secondary sulfate and nitrate. But they showed better performance in simulating average concentrations and temporal variations of input species, particularly for primary source markers, than the PMFwₕₒₗₑ solution. After removing event data, PMF modeling was conducted for individual months (PMFₘₒₙₜₕ) and the 4-year period (PMF₄₋yₑₐᵣ), respectively. PMFₘₒₙₜₕ solutions reflected varied source profiles and contributions and reproduced monthly variations of input species better than the PMF₄₋yₑₐᵣ solution, but failed to capture seasonal patterns of secondary salts. Additionally, four winter pollution days were selected for hour-by-hour PMF simulations, and three sample sizes (500, 1000, and 2000) were tested using a moving window method. The results showed that using short-term observation data performed better in reflecting immediate changes in primary sources, which will benefit future air quality control when primary PM emissions begin to increase.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM₂.₅, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960–2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]). We analyzed daily dust storm frequency (DSF) at the 10-day scale (first [FTDM], middle [MTDM], and last [LTDM] 10 days of a month), and investigated the association of dust storm occurrences with meteorological factors. The 10-day DSF was greatest in the FTDM (accounting for 77.14% of monthly occurrences) in the WR, MTDM (45.85%) in the MR, and LTDM (72.12%) in the ER, showing a clear trend of movement from the WR to the ER. Temporal analysis of DSF revealed trend changes over time at annual and 10-day scales, with mutation points at 1985 and 2000. We applied single-factor and multiple-factor analyses to explore the driving mechanisms of DSF at the 10-day scale. Among single factors, a low wind-speed threshold, high solar radiation, and high evaporation were correlated with a high DSF, effectively explaining the variations in DSF at the 10-day scale; however, temperature, relative humidity, and precipitation poorly explained variations in DSF. Similarly, multiple-factor analysis using a classification and regression tree revealed that maximum wind speed was a major influencing factor of dust storm occurrence at the 10-day scale, followed by relative humidity, evaporation, and solar radiation; temperature and precipitation had weak influences. These findings help clarify the mechanisms of dust storm occurrence in East Asia.

Elemental concentrations of ambient aerosols are commonly sampled over 12–24 h, and the low time resolution puts a great limit on current understanding about the temporal variations and source apportionment based on receptor models. In this work, hourly-resolved concentrations of eighteen elements in PM₂.₅ at an urban site in Nanjing, a megacity in Yangtze River Delta of east China, were obtained by using a Xact 625 ambient metals monitor from 12/12/2016 to 12/31/2017. The influence of traffic activities was clearly reflected by the spikes of crustal elements (e.g., Fe, Ca, and Si) in the morning rush hour, and the firework burning and sandstorm events during the sampling periods were tracked by sharp enrichment of Ba, K and Fe, Ca, Si, Ti in PM₂.₅, respectively. To evaluate the advantage of hourly-resolved elements data in identifying impacts from specific emission sources, positive matrix factorization (PMF) analysis was performed with the 1-h data set (PMF₁₋ₕ) and 23-h averaged data (PMF₂₃₋ₕ), respectively. The 4- and 6-factor PMF₂₃₋ₕ solutions had similar factor profiles and consistent factor contributions as the corresponding PMF₁₋ₕ solutions. However, due to the limit in inter-sample variability, PMF analysis with 23-h average data misclassified some major (e.g., K, Fe, Zn, Ca, and Si) and trace (e.g., Pb) elements in factor profiles, resulting in different absolute factor contributions between PMF₂₃₋ₕ and PMF₁₋ₕ solutions. These results suggested the use of high time-resolved data to obtain valid and robust source apportionment results.