Application and validation of the fugitive dust source emission inventory compilation method in Xiong'an New Area, China

Environmental and Health Risk Assessment of Fugitive Dust from Magnesium Slag Yards

During the natural cooling process of magnesium slag stockpiles in the open air, the phase transformation of gamma-dicalcium silicate (γ-C2S) induces a powdering phenomenon, resulting in the generation of a large amount of PM10 and PM2.5 dust. Based on the dust emission model of stockpiles and the Gaussian dispersion model, combined with the Monte Carlo simulation method, this study conducted a quantitative assessment of the environmental risk of heavy metals (Pb, Cd, Hg, As, Cr(VI)) in dust to the surrounding residential areas. The results show that the enrichment degree of heavy metals in PM2.5 is significantly higher than that in PM10. At a downwind distance of 1000 m, the exceedance multiples of Cr(VI), As, and Cd reach 131.5, 23.6, and 51.8 times, respectively. The total carcinogenic risk (9.2 × 10-7) and total non-carcinogenic hazard quotient (0.15) in the residential area are below the limits, but the contribution rates of As and Cd are relatively high. Sensitivity analysis further reveals that the moisture content of the stockpile, dust removal rate, and distance are the key control parameters affecting the environmental risk. Based on the research findings, it is recommended to increase sprinkling frequency, install windbreak nets, and promote magnesium slag utilization to effectively control dust risks.

A district-level emission inventory of anthropogenic PM2.5 from the primary sources over the Indian Indo Gangetic Plain: Identification of the emission hotspots

A district-wise emission inventory was made for the states and union territories (UTs) of the Indian Indo-Gangetic Plain for the base year of 2018 to estimate the emissions of PM2.5 from various sectors. In addition to conventional sectors, emissions from road dust, fossil-fuelled irrigation pumps, and construction dust were also taken into account. Total primary anthropogenic PM2.5 emission was estimated to be 3157.3 Gg (or kilo-tones) for the year 2018 of which 32 % originated from the industrial sector, 27 % from domestic fuel consumption, 23 % from open burning, 14 % from road dust, 2 % from vehicular and 2 % from various unorganized sectors. The highest emissions were observed during the premonsoon (1013 Gg/year) followed by postmonsoon (802Gg/year), winter (788 Gg/year), and lowest during the monsoon (554Gg/year). Among the states and UTs, Uttar Pradesh contributes the most in total emissions (39 %), followed by Punjab (19 %), Bihar (17 %), West Bengal (13 %), Haryana (11 %), Delhi (0.9 %) and Chandigarh (0.1 %). Emission for per capita and for billion-rupee of state gross domestic product (GDP) were the highest for Punjab and Haryana. Results have identified the districts of Punjab (Firozpur, Ludhiana, Jalandhar), scattered pockets of Uttar Pradesh (Sonbhadra, Agra, Varanasi, Kanpur, Lucknow, Prayagraj) and lower Gangetic delta (Gaya, Muzaffarpur, Burdwan, both 24-parganas and Murshidabad) as potent hotspots of cumulative PM2.5 emissions. On the other hand, the districts of Punjab (Faridkot, Mansa, Muktsar, Fatehgarh) were found to be the hotspots for per capita emissions. High emissions were observed from the domestic sector, brick kilns, and micro and small-scale industries, and regulating norms should be more stringent for these sectors. Such a study will be a value add for the policymakers and health experts to assess emission hot spots, pollution simulation, and associated mortality analysis of the region.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Bottom-up and top-down approaches for estimating road dust emission and correlating it with a receptor model results over a typical urban atmosphere of Indo Gangetic Plain

To investigate the emission and concentration of PM10 and PM2.5-related road dust over Agra, a typical semi-arid urban atmosphere of the Indo Gangetic Plain (IGP), a fine-resolution emission inventory and receptor modeling-based source apportionment was undertaken for the year 2019. On-road, the silt load of Agra (7-55 g/m2 of the road) was found to be 10 to 50 times higher than that reported in advanced countries. The silt load over Agra varied widely depending on road conditions, long-range transport, and land-use pattern. Depending on the silt load, land-use and fleet averaged weight, the annual emission factor for road dust was estimated as 14.3 ± 3.2 (PM10) and 4.4 ± 1.4 (PM2.5) gm/VKT (vehicle kilometer travel). PM10 emission of road dust alone contributed 80 % (29 ± 6 t/d) to the total emission of PM10 and 68 % (9 ± 3 t/d) to PM2.5 of the city with the maximum emission being in industrial areas. Chemical analysis of ambient PM10, PM2.5, and road dust samples showed that the road dust was enriched with geogenic components and was in good agreement with the road dust profile identified from the positive matrix factorization receptor model. The model estimated contribution of road dust (summer and winter combined) to PM10 and PM2.5 ambient air levels was 28 % (67 μg/m3) and 23 % (27 μg/m3) respectively. Summer showed a larger road dust contribution than winter due to strong surface wind and dry road conditions. Results have revealed that the emissions and concentrations of road dust are closely interrelated with road conditions (silt load), land-use patterns, VKT, weight of the vehicles, and micrometeorological conditions. The large road dust emission in IGP cities requires better road conditions and traffic management to curb the emission.

Temporal-spatial distributions of road silt loadings and fugitive road dust emissions in Beijing from 2019 to 2020

Road silt loading (sL) is an important parameter in the fugitive road dust (FRD) emissions. In this study, the improved Testing Re-entrained Aerosol Kinetic Emissions from Roads (TRAKER) combined with the AP-42 method was firstly developed to quickly measure and estimate the sLs of paved roads in Beijing, China. The annual average sLs in Beijing was 0.59±0.31 g/m² in 2020, and decreased by 22.4% compared with that in 2019. The seasonal variations of sLs followed the order of spring > winter > summer > autumn in the two years. The seasonal mean road sLs on the same type road in the four seasons presented a decline trend from 2019 to 2020, especially on the Express way, decreasing 47.4%-72.7%. The road sLs on the different type roads in the same season followed the order of Major arterial ∼ Minor arterial ∼ Branch road > Express road, and Township road ∼ Country highway > Provincial highway ∼ National highway. The emission intensities of PM₁₀ and PM_2.5 from FRD in Beijing in 2020 were lower than those in 2019. The PM₁₀ and PM_2.5 emission intensities at the four planning areas in the two years all presented the order of the capital functional core area > the urban functional expansion area > the urban development new area > the ecological conservation and development area. The annual emissions of PM₁₀ and PM_2.5 from FRD in Beijing in 2020 were 74,886 ton and 18,118 ton, respectively, decreasing by ∼33.3% compared with those in 2019.

Morphology and Dust-Suppression Evaluation of Fugitive Dust Particles in Beijing

Fugitive dust particles are important contributors to urban ambient particulate matter (PM), while their emissions have been ignored or greatly underestimated in previous studies, leading to the underestimation of PM concentrations and health impacts. Thus, studying the morphology of fugitive dust, taking appropriate dust-suppression measures, and evaluating dust-suppression effects are crucial to the prevention and control of fugitive dust. In this study, we investigated the morphology and composition of dust particles from different dust sources, including bare land, stock dump, construction, and road dust. Afterwards, different dust-suppression measures including fence interception nets, bare ground mesh nets, and road dust-suppressants were undertaken to simulate and analyze their dust-suppression effects. Finally, the height concentration profiling method was used to comprehensively evaluate the on-site dust-suppression effect, which can not only accurately evaluate the dust-suppression effect, but also predict the dust-suppression ability in a wide range. Gaining insights into the morphology and composition of dust from representative sources is an important step forward to prevent and control fugitive dust, and selecting an appropriate dust-suppression effect evaluation method will provide a beneficial guide for effectively controlling PM pollution in the future.

Characteristics of unorganized emissions of microplastics from road fugitive dust in urban mining bases

Nowadays, plastic pollution attracts attention, while data on microplastic (MP) emissions to atmosphere from urban mining bases are rarely quantified. In this paper, the emission characteristics of MPs in road fugitive dust from two urban mining bases were studied, the emission factors of MPs from road fugitive dust source were updated, and emission inventories were established. It is estimated that the waste glass recycling base and the e-waste dismantling plant emit 1265.53 g and 40.5 g of MPs into atmosphere respectively each year, with the highest percentage of tire micro-rubber. The roads with the most emissions are located in the middle of warehouses and workshops. Emission factors for MPs mainly depend on average vehicle weight, and heavy vehicles cause more MP emissions. Uncertainty analysis of the inventory indicated that the random error of MP emissions on a single road in waste glass recycling base was -79.1%-187.1%, while in e-waste dismantling plant was -62.7%-102.05%, which is mainly related to the silt loading. This study completes the quantitative data on the unorganized emissions of MPs from road fugitive dust in these two typical urban mining bases, and provides guidance for air pollution prevention and control.