Air quality impact assessment of multiple open pit coal mines in northern Colombia

Huertas, José Ignacio; Huertas, María E.; Izquierdo, Sebastián; González, Efraín Salas

doi:10.1016/j.jenvman.2011.08.007

Cited by 113 publications

(58 citation statements)

References 16 publications

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“…EPA, 2004). It is a new regulatory dispersion model in place of its predecessor the Industrial Sources Complex Short Term (ISCST) model, and it has been used in environmental impact assessment (Huertas et al, 2012;Perry et al, 2005;Silverman and Tell, 2007;Venkatram et al, 2001). The system consists of four main processors: AERMAP as the terrain processor, AERSURFACE as the surface property/ characteristic processor, AERMET as the meteorological processor, and AERMOD as the main dispersion model processor.…”

Section: Model Setupmentioning

confidence: 99%

NO₂ and SO₂dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

Chusai

Manomaiphiboon

Saiyasitpanich

et al. 2012

Journal of the Air & Waste Management Association

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Map Ta Phut industrial area (MA) is the largest industrial complex in Thailand. There has been concern about many air pollutants over this area. Air quality management for the area is known to be difficult, due to lack of understanding of how emissions from different sources or sectors (e.g., industrial, power plant, transportation, and residential) contribute to air quality degradation in the area. In this study, a dispersion study of NO 2 and SO 2 was conducted using the AERMOD model. The areaspecific emission inventories of NO x and SO 2 were prepared, including both stack and nonstack sources, and divided into 11 emission groups. Annual simulations were performed for the year 2006. Modeled concentrations were evaluated with observations. Underestimation of both pollutants was found, and stack emission estimates were scaled to improve the modeled results before quantifying relative roles of individual emission groups to ambient concentration over four selected impacted areas (two are residential and the others are highly industrialized). Two concentration measures (i.e., annual average area-wide concentration or AC, and area-wide robust highest concentration or AR) were used to aggregately represent mean and high-end concentrations for each individual area, respectively. For AC-NO 2 , on-road mobile emissions were found to be the largest contributor in the two residential areas (36-38% of total AC-NO 2 ), while petrochemical-industry emissions play the most important role in the two industrialized areas (34-51%). For AR-NO 2 , biomass burning has the most influence in all impacted areas (>90%) except for one residential area where on-road mobile is the largest (75%). For AC-SO 2 , the petrochemical industry contributes most in all impacted areas (38-56%). For AR-SO 2 , the results vary. Since the petrochemical industry was often identified as the major contributor despite not being the largest emitter, air quality workers should pay special attention to this emission group when managing air quality for the MA.Implications: Effective air quality management in Map Ta Phut Industrial Area, Thailand requires better understanding of how emissions from various sources contribute to the degradation of ambient air quality. Based on the dispersion study here, petrochemical industry was generally identified as the major contributor to ambient NO 2 and SO 2 . By accounting for all stack and non-stack sources, on-road mobile emissions were found to be important in some particular areas.

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Section: Model Setupmentioning

confidence: 99%

NO₂ and SO₂dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

Chusai

Manomaiphiboon

Saiyasitpanich

et al. 2012

Journal of the Air & Waste Management Association

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“…This step does not change R 2 . According to the sensitivity analysis reported by Huertas et al (2012a), this constant should be approximately 6.65. In this step, the background concentration is also considered, such that M B -b & 0.…”

Section: Resultsmentioning

confidence: 99%

“…Since 2007, this network has been reporting readings that exceed the standards for daily and annual TSP and PM 10 concentrations in some nearby villages (Huertas et al 2012a). To assess the impact of mining operations on Table 1 Metrics to evaluate the capability of air quality models to reproduce, in time and space at surface level, experimental observations (Mosca et al 1998;Walker et al 1999;ASTM 2000;Li et al 2011;Zawar-Reza et al 2005) Metric Definition Perfect value…”

Section: Open Pit Mining In Northern Colombiamentioning

confidence: 99%

“…Tables 2 and 3 show the emission inventory and the meteorological data used as inputs. The results of the study were used to estimate the contribution of each mine to pollution in each village within the mining region, thereby allowing the environmental authority to determine the appropriate contribution of each mining company towards financing decontamination measures (Huertas et al 2012a). The accuracy of the results obtained by the Colombian environmental authority depends directly on the accuracy of the emission data.…”

Section: Range Meaningmentioning

confidence: 99%

See 1 more Smart Citation

Assessing precision and accuracy of atmospheric emission inventories

Huertas

Díaz

2012

Int. J. Environ. Sci. Technol.

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Assuming that state-of-the-art air quality models are accurate, then the precision and accuracy of their results directly depend on the precision and accuracy of their geographical, meteorological and emission input data. There are important applications, such as open pit mining, in which emission data are the main source of uncertainty. In such cases, historical air quality experimental data are typically available. The present work proposes a backward air quality simulation approach to assess the accuracy of emission inventories for these applications, with the goal of identifying sources that are over or underestimated. This approach consists of finding constants of the linear combination of the estimated emission that maximize R 2 and make the slope equal to one in the linear correlation analysis when the results from the air quality model are compared to the experimental measurements of air quality. This methodology was applied to the case of the mining region in northern Colombia. As one of the largest open pit coal mining regions in the world, this region consists of seven independent mines with no relevant additional sources of emission. Use of the proposed methodology allowed quantification of the amount by which companies over or underestimated their emission, as well as quantification of uncertainties due to sources not considered in the model but that locally affect each monitoring station.

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“…Huertas et al conducted an assessment of air quality impact on multiple open pit coal mines in northern Colombia by using Screen3. Based on the results from the model, areas within the modeling region were identified as highly, fairly, moderately and marginally polluted, according to local regulations (47). Based on the results of a study by Visscher, the concentration of nitrogen dioxide emission to the air is independently related to the emission source.…”

Section: Discussionmentioning

confidence: 99%

Dispersion Modeling of Nitrogen Dioxide in Ambient Air of Ahvaz City

et al. 2016

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Background: Air emission modeling is used to study how pollutants are spread in the environment and to forecast their emissions rate in different climatic conditions in the study area. Today, air pollution threatens human public health and the environment, and Ahvaz is considered one of the main centers of air pollution in Iran. Objectives: This aim of this study was to evaluate the nitrogen dioxide distribution in Ahvaz by using the software program Screen 3. Materials and Methods: This analytical study was conducted in 2013 in Ahvaz. Data collections were performed by the Ahvaz Department of Environment. After processing the data received (averaging instruction set, coding), the file was converted to an input file for the software program Screen 3. In the next phase, study of the distribution and release of nitrogen dioxide during the year 2013 was modeled using the Screen 3 software. Results: Based on the results of this study, the highest and the lowest nitrogen dioxide concentration were in the Bureau of Meteorology and Head office of ADoE stations, respectively. The maximum concentration of NO 2 , at a height of 45 m, was 20 μg/m 3 .According to the findings of the modeling, examination of the combustion process was observed, and this was caused by the amount of nitrogen dioxide emitted by flue outlets, which was affected by temperature and pressure, fuel flow rates and inlet air temperature. Also, results showed that in most cases emission of air pollutants was towards the southwest. Conclusions: Results of this study showed that the Screen3 software is one of the popular models for estimating the distribution of air pollutants. Nitrogen dioxide emissions are highly regulated in most industrialized regions. Decreased emissions from fuel combustion, the use of scrubbers and filters that absorb pollution can be very useful for reduction and control of nitrogen dioxide emissions.

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Air quality impact assessment of multiple open pit coal mines in northern Colombia

Cited by 113 publications

References 16 publications

NO₂ and SO₂dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

NO₂ and SO₂dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

Assessing precision and accuracy of atmospheric emission inventories

Dispersion Modeling of Nitrogen Dioxide in Ambient Air of Ahvaz City

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Air quality impact assessment of multiple open pit coal mines in northern Colombia

Cited by 113 publications

References 16 publications

NO2 and SO2dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

NO2 and SO2dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

Assessing precision and accuracy of atmospheric emission inventories

Dispersion Modeling of Nitrogen Dioxide in Ambient Air of Ahvaz City

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Product

Resources

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NO₂ and SO₂dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand

NO₂ and SO₂dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand