Air quality modeling for effective environmental management in the mining region

Space-specific flux estimation of atmospheric chemicals from point sources to soil

Predicting chemical flux to soil from industrial point sources accurately at a regional scale has been a significant challenge due to high uncertainty in spatial heterogeneity and quantification. To address this challenge, we developed an innovative approach by combining California Air Resources Board Puff (CALPUFF) and mass balance models, leveraging their complementary strengths in quantitative accuracy and spatial precision. Specifically, CALPUFF was used to predict the polycyclic aromatic hydrocarbons (PAHs) flux to soil due to industrial sources. Additionally, the spatial distribution coefficient of PAHs flux (e.g., si for spatial unit i) was calculated by neural network and combined with the mass balance model to obtain the results of total PAHs fluxes, which were then combined with the results predicted by CALPUFF to effectively estimate the contribution of industrial sources to soil PAHs flux. Taking a petrochemical industry region located in Zhejiang province, China as a case study, results showed the input Phenanthrene (Phe) and Benzo(a)pyrene (BaP) fluxes predicted by CALPUFF were generally lower than those by the mass balance model, with slightly different distribution patterns. CALPUFF results, based on 36 industrial sources, partially represent those of the mass balance model, which includes all sources and pathways. It was suggested that industrial sources contributed 49%-89% and 65%-100% of soil Phe and BaP, respectively across the study area. The average Phe flux from point sources by deposition averaged 2.68 mg m-2∙a-1 in 2021, accounting for approximately 60% of the total Phe flux to soil. The average BaP flux from point sources by deposition averaged 0.0755 mg m-2∙a-1, accounting for only 0.1%-3.65% of the total BaP flux to soil. Thereby, our approach fills up a gap between the relevance to point sources and the accuracy of deposition quantification in estimating chemical flux from specific point sources to soil at a regional scale.

Mineral wealth paradox: health challenges and environmental risks in African resource-rich areas

BACKGROUND

Africa is blessed with vast arable land and enriched with valuable natural resources encompassing both renewable (like water, forests, and fisheries) and non-renewable (such as minerals, coal, gas, and oil). Under the right conditions, a natural resource boom should serve as an important driver for growth, development, and the transition from cottage industry to factory output. However, despite its wealth, Africa is often associated with the notion of a resource curse. Negative outcomes are often linked with mineral wealth. This paper investigates the causes of adverse health outcomes in resource-rich regions. The study provides empirical support for the natural resource curse with particular emphasis on the environmental health risks in Africa. We explore the multifaceted connections among mineral deposits, environmental risks, conflict events and population dynamics, shedding light on the complexities of resource-rich areas.

RESULTS

We amalgamate georeferenced data pertaining to 22 specific mineral deposits with information on the prevalence of reliance on compromised infrastructures at a spatial resolution of 0.5∘ × 0 . 5 ∘ for all of Africa between 2000 and 2017. Through comprehensive econometric analysis of environmental health risk factors, including reliance on contaminated water sources, open defecation, unimproved sanitation, particulate matter concentration, and carbon concentration, we uncover the intricate pathways through which mineral deposits impact public health. Our findings revealed the significant role of in-migration in mediating environmental health risks. Moreover, we found that the activities of extractive companies amplify certain environmental risks including reliance on unimproved sanitation and practices and particulate matter concentration. Conflict events emerge as a key mediator across all environmental health risks, underlining the far-reaching consequences of instability and violence on both local communities and the environment.

CONCLUSION

The study contributes to the discourse on sustainable development by unraveling the nuanced associations between mineral wealth and health challenges. By drawing attention to the intricate web of factors at play, we provide a foundation for targeted interventions that address the unique environmental and health challenges faced by mineral-rich communities.

Physical properties of odorants affect behavior of trained detection dogs during close-quarters searches

Trained detection dogs have a unique ability to find the sources of target odors in complex fluid environments. How dogs derive information about the source of an odor from an odor plume comprised of odorants with different physical properties, such as diffusivity, is currently unknown. Two volatile chemicals associated with explosive detection, ammonia (NH3, derived from ammonium nitrate-based explosives) and 2-ethyl-1-hexanol (2E1H, associated with composition C4 plastic explosives) were used to ascertain the effects of the physical properties of odorants on the search behavior and motion of trained dogs. NH3 has a diffusivity 3.6 times that of 2E1H. Fourteen civilian detection dogs were recruited to train on each target odorant using controlled odor mimic permeation systems as training aids over 6 weeks and then tested in a controlled-environment search trial where behavior, motion, and search success were analyzed. Our results indicate the target-odorant influences search motion and time spent in the stages of searching, with dogs spending more time in larger areas while localizing NH3. This aligns with the greater diffusivity of NH3 driving diffusion-dominated odor transport when dogs are close to the odor source in contrast to the advection-driven transport of 2E1H at the same distances.

An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines

Dust is a severe environmental issue in open-pit mines, and accurate estimation of its concentration allows for viable solutions for its control and management. This research proposes a machine learning-based solution for accurately estimating dust concentrations. The proposed approach, tested using real data from the Haerwusu open-pit coal mine in China, is based upon the integrated random forest-Markov chain (RF-MC) model. The random forest method is used for estimation, while the Markov chain is used for estimation correction. The wind speed, temperature, humidity, and atmospheric pressure are used as inputs, while PM2.5, PM10, and TSP are taken as estimated outputs. A detailed procedure for implementing the RF-MC is presented, and the estimated performance is analyzed. The results show that after correction, the root mean squared error significantly decreased from 7.40 to 2.56 μg/m3 for PM2.5, from 15.73 to 5.28 μg/m3 for PM10, and from 18.99 to 6.27 μg/m3 for TSP, and the Pearson correlation coefficient and the mean absolute error also improved considerably. This work provides an improved machine learning approach for dust concentration estimation in open-pit coal mines, with a greater emphasis on simplicity and rapid model updates, which is more applicable to ensure the prudent use of water resources and overall environmental conservation, both of which are advantageous to green mining.

Environmental Risk Assessment for PM2.5 Pollution from Non-Point Sources in the Mining Area Based on Multi-Source Superposition and Diffusion

To identify high-concentration contributing sources and highly dispersive pollution sources of fine particulate matter, analyze the relationship between the location and distribution shape of emission sources and the concentration contribution and dispersion of particulate matter, and realize the atmospheric environment risk simulation and the differential control of non-point sources in the mining area, taking a large mining area in Inner Mongolia as an example, we classified the emission sources of PM2.5 (particulate matter less than 2.5 μm) and complied with the emission inventory. A CALPUFF model was used to simulate the contribution for the PM2.5 concentration of six types of emission sources and a multi-source superposition. Through scenario simulation, we analyzed the relationship between the spatial distribution of emission sources and the emission concentration and diffusion in a large mining area. We analyzed the relative risks of six types of sources under the influence of other superimposed sources and the change of emission concentration during transmission. We established a three-dimensional evaluation model to assess the atmospheric environmental risk of PM2.5 non-point sources in the mining area, considering the change rate of PM2.5 concentration with migration, the relative contribution ratio of superimposed sources, and the equal contribution index of the standard concentration. The results show that the maximum equal contribution index of standard concentration of multi-source superposition was 4.40. Among them, non-paved roads, exposed surface sources of coal piles, and exposed surface sources of mine pits and dumps were the top three pollution contributors, and their maximum equal contribution indexes of standard concentration were 2.40, 2.21, and 2.10, respectively. The effect of superimposed pollution sources was affected by the wind field and the spatial distribution density of emission sources, while the dispersion was affected by the wind direction and the distribution direction of pollution sources. In the case of the same source intensity and emission area, the denser the source distribution was, the higher the emission concentration was, the smaller the contribution ratio of superimposed sources was, and the greater the relative pollution risk was. When the angle between the direction of dispersed linear pollution sources and the dominant wind direction was smaller, the emission concentration was higher, but the diffusion surface was smaller. When the angle with the direction of wind direction was larger, the emission concentration was lower, but the diffusion surface was larger. Concentrated pollution sources had the highest concentration and the diffusion surface was in the middle. Non-paved roads had the highest environmental risk, with an average risk of 5.61 × 10−2, followed by coal piles with an average value of 2.06 × 10−2, followed by pits and dumps with an average value of 1.89 × 10−2; the environmental risk of loading and unloading sources was the lowest. Unpaved roads were pollution sources with high relative pollution risk and diffusion risk, and their average relative pollution risk and diffusion risk were 2.34 × 10−2 and 3.28 × 10−2, respectively. In the case of multi-source superposition, the high-risk areas were mainly heavily polluted areas with intensive emission sources, while the medium-risk areas were moderately polluted areas with scattered pollution sources, and the diffusion risk was high. This research concludes that the dispersed distribution of pollution sources can reduce pollution risk, and the smaller the angle is between the linear distribution direction of pollution sources and the dominant wind direction, the smaller the diffusion risk is. Therefore, differentiated control can be carried out according to the characteristics of pollution sources. The conclusions can provide methods and theoretical support for the control of atmospheric environment risk, pollution prevention, and control planning in mining areas.

Recent and historical pollution legacy in high altitude Lake Marboré (Central Pyrenees): A record of mining and smelting since pre-Roman times in the Iberian Peninsula

We have analyzed potential harmful trace elements (PHTE; Pb, Hg, Zn, As and Cu) on sediment cores retrieved from lake Marboré (LM) (2612 m a.s.l, 42°41'N; 0° 2'E). PHTE variability allowed us to reconstruct the timing and magnitude of trace metal pollutants fluxes over the last 3000 years in the Central Pyrenees. A statistical treatment of the dataset (PCA) enabled us to discern the depositional processes of PHTE, that reach the lake via direct atmospheric deposition. Indeed, the location of LM above the atmospheric boundary layer makes this lake an exceptional site to record the long-range transport of atmospheric pollutants in the free troposphere. Air masses back-trajectories analyses enabled us to understand the transport pathways of atmospheric pollutants while lead isotopic analyses contributed to evaluate the source areas of metal pollution in SW Europe during the Late Holocene. PHTE variability, shows a clear agreement with the main exploitation phases of metal resources in Southern Europe during the Pre-Industrial Period. We observed an abrupt lead enrichment from 20 to 375 yrs CE mostly associated to silver and lead mining and smelting practices in Southern Iberia during the Roman Empire. This geochemical data suggests that regional atmospheric metal pollution during the Roman times rivalled the Industrial Period. PHTE also increased during the High and Late Middle Ages (10-15^th centuries) associated to a reactivation of mining and metallurgy activities in high altitude Pyrenean mining sites during climate amelioration phases. Atmospheric mercury deposition in the Lake Marboré record mostly reflects global emissions, particularly from Almadén mines (central Spain) and slightly fluctuates during the last three millennia with a significant increase during the last five centuries. Our findings reveal a strong mining-related pollution legacy in alpine lakes and watersheds that needs to be considered in management plans for mountain ecosystems as global warming and human pressure effects may contribute to their future degradation.

Considering environmental variables in the design of waste dumpsites

Mining generates a significant quantity of waste material including ballast, gravel, and slags, which are often deposited in areas without taking into account the environment impacts and the need to ensure the physical and chemical stability of the disposed waste. One of the less studied problems is the emission of particulate matter produced by wind erosion at the dumpsites. This erosion is mainly caused by two factors, wind speed and turbulence, due to surface phenomenon. Until now, the design of waste dumpsites in the Chilean mining industry has not considered these environmental conditions. Efforts to minimize disposal costs have always been achieved by depositing ballast without considering these variables. When wind impacts this unprotected surface, it creates a source of dust that requires some special attention. The problem that this research tries to solve is to reduce particulate material to the atmosphere from waste dumps in which, under certain atmospherics and geographic conditions, specifically on winter season, its concentrations overpass the maximum limit allowed by law, generating bronchopulmonary diseases and even closing partially or totally mine operation. The result is the creation of a waste dumpsite design model, with its corresponding algorithms, which will allow optimization of the waste dumpsite design. From these results, future researches could explore more sustainable mining, such as unit operations, drilling, blasting, load and crushing material, reducing particulate material emissions to the atmosphere, and minimizing environmental impact due to exploitation.