Characterising particulate matter source contributions in the pollution control zone of mining and related industries using bivariate statistical techniques

Stone mining work and dust pollution in Birbhum district, West Bengal, India

Dust pollution is common in Indian roads and several industrial settings (including mines) that affects human health. Identification and characterization of the dust particles in the mining area is essential for knowing the properties of the dust that effectively causes ailments to humans, particularly among workers those who are working in unorganized industrial settings. The present study aimed to determine the level of dust pollution and to know the size and characterize the dust particles in the Pachami-Hatgacha stone mine areas of Birbhum district, West Bengal, India. Dust samples were collected and analysed for Dynamic Light Scattering (DLS) to determine the size and shape of the particles, Fourier Transform Infrared Spectroscopy (FT-IR) to determine the free silica content, and X-ray Florence (XRF) analysis for quantitative estimation of components in the sample. All the analyses were done following standard instrumentation and techniques. The size of the dust particles was much less (ranges 101-298 nm) than the size of respirable particles (2500 nm). Those were mostly generated as well as precipitated during peak working hours of the day. Presence of considerable amounts of silica was confirmed by the FT-IR (strong and broad band at 1000 cm-1) and XRF analysis (76.85% SiO2). Exposure to these dust particles may cause severe health impairments. Therefore, interventions like wet drilling and blasting, sprinkling of water during peak working hours, and awareness of use of personal protective devices among workers are required to reduce the risk and hazards associated with dust pollution to the health of miners and inhabitants around the mines.

Bark, soil and lichens are effective indicators of dust from limestone industries in Thailand

Cement production, quarrying and stone crushing are major emission sources of alkaline dust that can affect human health and vegetation. The main objectives of this study were to evaluate the potential of using bark pH, soil pH and lichen community for indicating alkaline dust pollution. Twelve polluted sites were in a limestone industrial area. Bark pH and the lichen community were observed on Alstonia scholaris trees, and soil pH was obtained from topsoil samples. The bark pH at all polluted sites was significantly higher (5.5 to 7.3) than that at the unpolluted site (4.3). Among the polluted sites, the highest bark pH value was observed at the nearest site to the center of the industrial area, while the lowest value was discovered at the farthest site. Bark pH showed a strongly negative correlation with the distance from the center. Soil pH at the unpolluted site (6.3) was also significantly lower than that at the polluted sites (7.6 to 8.1), except at the farthest site (6.5). The soil pH also tended to increase closer to the center. Seven lichen species were observed on the trunks of investigated trees in all polluted sites and were observed only at sites more than 4.7 km away from the center, where bark pH ranged from 5.5 to 6.3. The extent of dust impact on vegetation seemed to be within 6-7 km from the center. The results of this study confirm the potential of the bark pH of A. scholaris, soil pH and lichen community as long-term indicators of alkaline dust pollution.

Immission levels and identification of sulfur dioxide sources in La Oroya city, Peruvian Andes

La Oroya is a city in the Peruvian Andes that has suffered a serious deterioration in its air quality, especially due to the high rate of sulfur dioxide (SO2) emissions, which underlines the importance of knowing its sources of contamination and variation over the years. In this sense, this study aimed to evaluate the immission levels and determine the sources of SO2 contamination in La Oroya. This analysis was performed using the hourly concentration data of SO2, and meteorological variables (wind speed and direction), which were analyzed for a period of three years (2018-2020). Graphs of time series, wind and pollutant roses, bivariate polar graphs, clustering k-means, nonparametric statistical tests, and the application of the conditional bivariate probability function were performed to analyze the data and identify the emission sources. The mean concentration of SO2 was 264.2 μg m-3 for the study period, where 55.66 and 2.37% of the evaluated days exceeded the guideline values recommended by the World Health Organization and the Peruvian Environmental Quality Standard for air for 24 h, respectively. The results showed a defined pattern for the daily and monthly variations, with peaks in the morning hours (0900-1000 h LT) and at the end of the year (December), respectively. The main sources of SO2 emissions identified were light and heavy vehicles that travel through the Central Highway, the La Oroya Metallurgical Complex, the transit of vehicles within the city, and the diesel-electric locomotives that provide cargo transportation services and tourism passenger transportation. The article attempts to contribute to the development of adequate air quality management policies.

Analyzing Characteristics of Particulate Matter Pollution in Open-Pit Coal Mines: Implications for Green Mining

The particulate pollution in the open-pit coal mines of China is particularly severe in winter. The aim of this study is to understand the pollution characteristics of particulate matter (PM) in winter and provide a basis for the prevention and control of particulate pollution. We took the problem of PM concentration at the bottom of the Haerwusu Open-pit Coal Mine (HOCM) as the research object. Dust monitoring equipment at two measurement points at different heights were positioned for continuous monitoring of the PM concentration. The data for three months were gathered. Statistical analyses were performed to analyze the variation characteristics of the PM and its relationship with meteorological factors. The results show that the average PM concentration in the study area is below the average daily limit of the China National Ambient Air Quality Standard (GB 3095-2012). However, the average concentration of PM10 exceeded the national limit in December. The order of PM concentration is observed as December > January > February. The correlation of PM is found to be positive with humidity and negative with wind speed. Temperature is found to be positively correlated with PM in December, while it is negative in January. At the same time, the temperature difference in December is negatively correlated with PM concentration. Under the combined action of multiple meteorological factors, the magnitude of the impact on the PM concentration at the bottom of the pit in winter is humidity > temperature > wind speed > temperature difference (inverse temperature intensity). In conclusion, PM2.5 is found to be more sensitive to environmental factors. The results of this study are particularly useful to progress in green mining.