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.

HONO is an important component of reactive nitrogen (Nᵣ) and precursors of OH radical. However, the source and removal of HONO are not clear. Here, measurements of HONO (May 18–31, 2018) were conducted in Xi'an and Xianyang simultaneously for the first time. The relationship between HONO and other Nᵣ (such as NO and NO₂) in two cities was analyzed. The mixing ratio of HONO in Xi'an was 1.2 ± 0.8 ppbv, and that in Xianyang was 1.2 ± 1.1 ppbv. The nighttime HONO mixing ratio was higher in Xianyang, while the daytime HONO was higher in Xi'an. Compared with the contribution from heterogeneous process of NO₂, direct emissions and homogeneous processes (NO + OH) were less important for nocturnal HONO formation in these two cities. The relative contribution of heterogeneous process in Xianyang was more important than that in Xi'an. The reaction of NO₂ upon aerosols surface was identified as an important source of HONO for two sites. The conversion of NO₂ on the other surfaces might attend the heterogeneous formation of HONO in Xianyang site. Daytime HONO budget analysis indicated that there was an additional unknown formation process of HONO at two sites. The net OH production rate from HONO (from 08:00 to 17:00) was 1.6 × 10⁷ and 1.3 × 10⁷ molecule/(cm³ s) for Xian and Xianyang, 5.2 and 3.5 times higher than from O₃ photolysis. Besides, a dust storm appeared during this observation period, and the impact of local emission and transport processes was separately analyzed. The sources, characteristics, and effects of HONO identified in this study laid a foundation for further research on HONO and air pollution in the Guanzhong area.

To identify the sources and heterogeneous reactions of sulfate and nitrate with dust in the atmosphere, airborne particles in Xi'an, inland China during the spring of 2017 were collected and measured for chemical compositions, along with a laboratory simulation of the heterogeneous formation of ammonium nitrate on the dust surface. Our results showed that concentrations of Ca²⁺, Na⁺ and Cl⁻ in the TSP samples were enhanced in the dust events, with the values of 41.8, 5.4 and 4.0 μg m⁻³, respectively, while NO₃⁻ (7.1 μg m⁻³) and NH₄⁺ (2.4 μg m⁻³) remarkably decreased, compared to those in the non-dust periods. During the dust events, NH₄⁺ correlated only with NO₃⁻ (R² = 0.52) and abundantly occurred in the coarse mode (>2.1 μm), in contrast to that in the non-dust periods, which well correlated with sulfate and nitrate and enriched in the fine mode (<2.1 μm). SO₄²⁻ in Xi'an during the dust events existed mostly as gypsum (CaSO₄·2H₂O) and mirabilite (Na₂SO₄·10H₂O) and dominated in the coarse mode, suggesting that they were directly transported from the upwind Gobi Desert region. Our laboratory simulation results showed that during the long-range transport hygroscopic salts in the Gobi dust such as mirabilite can absorb water vapor and form a liquid phase on the particle surface, then gaseous NH₃ and HNO₃ partition into the aqueous phase and form NH₄NO₃, resulting in the strong correlation of NH₄⁺ with NO₃⁻ and their accumulation on dust particles. The dry deposition flux of total inorganic nitrogen (NH₄⁺ + NO₃⁻) in Xi'an during the dust events was 0.97 mg-N m⁻² d⁻¹ and 37% higher than that in the non-dust periods. Such a significant enhanced N-deposition is ascribed to the heterogeneous formation of NH₄NO₃ on the dust particle surface, which has been ignored and should be included in future model simulations.

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.

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.

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.

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.

Size-segregated aerosol samples were collected in New Delhi, India from March 6 to April 6, 2012. Homologous series of n-alkanes (C19C33), n-fatty acids (C12C30) and n-alcohols (C16C32) were measured using gas chromatography/mass spectrometry. Results showed a high-variation in the concentrations and size distributions of these chemicals during non-haze, haze, and dust storm days. In general, n-alkanes, n-fatty acids and n-alcohols presented a bimodal distribution, peaking at 0.7–1.1 μm and 4.7–5.8 μm for fine modes and coarse modes, respectively. Overall, the particulate matter mainly existed in the coarse mode (≥2.1 μm), accounting for 64.8–68.5% of total aerosol mass. During the haze period, large-scale biomass burning emitted substantial fine hydrophilic smoke particles into the atmosphere, which leads to relatively larger GMDs (geometric mean diameter) of n-alkanes in the fine mode than those during the dust storms and non-haze periods. Additionally, the springtime dust storms transported a large quantity of coarse particles from surrounding or local areas into the atmosphere, enhancing organic aerosol concentration and inducing a remarkable size shift towards the coarse mode, which are consistent with the larger GMDs of most organic compounds especially in total and coarse modes. Our results suggest that fossil fuel combustion (e.g., vehicular and industrial exhaust), biomass burning, residential cooking, and microbial activities could be the major sources of lipid compounds in the urban atmosphere in New Delhi.

Although dust storms have been associated with adverse health outcomes, studies on the burden of dust storms on deaths are limited. As global warming has induced significant climate changes in recent decades, which have accelerated desertification worldwide, it is necessary to evaluate the burden of dust storm-induced premature mortality using a critical measure of disease burden, such as the years of life lost (YLL). The YLL attributable to dust storms have not been examined to date. This study investigated the association between Asian dust storms (ADS) and the YLL in Seoul, South Korea, during 2002–2013. We conducted a time-series study using a generalized additive model assuming a Gaussian distribution and applied a distributed lag model with a maximum lag of 5 days to investigate the delayed and cumulative effects of ADS on the YLL. We also conducted stratified analyses using the cause of death (respiratory and cardiovascular diseases) and sociodemographic status (sex, age, education level, occupation, and marital status). During the study period, 108 ADS events occurred, and the average daily YLL was 1511 years due to non-accidental causes. The cumulative ADS exposure over the 6-day lag period was associated with a significant increase of 104.7 (95% CI, 31.0–178.5 years) and 34.4 years (4.0–64.7 years) in the YLL due to non-accidental causes and cardiovascular mortality, respectively. Sociodemographic analyses revealed associations between ADS exposure and the YLL in males, both <65 and ≥ 65 years old, those with middle-level education, and the unemployed, unmarried, and widowed (26.5–83.8 years). This study provides new evidence suggesting that exposure to dust storms significantly increases the YLL. Our findings suggest that dust storms are a critical environmental risk affecting premature mortality. These results could contribute to the establishment of public health policies aimed at managing dust storm exposure and reducing premature deaths.

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.