Source Apportionment and Forecasting of Aerosol in a Steel City - Case Study of Rourkela

Urban air pollution is one of the biggest problems ascending due to rapid urbanization and industrialization. The improvement of air quality in an urban area in general, constitutes of three phases, monitoring, modeling and control measures. The present research work addresses the requirements of the urban air quality management programme (UAQMP) in Rourkela steel city. A typical UAQMP contains three aspects: monitoring of air pollution, modeling of air pollution and taking control measures. The present study aims to conduct the modeling of particulate air pollution for a steel city. Modeling of particulate matter (PM) pollution is nothing but the application of different mathematical models in source apportionment and forecasting of PM. PM (PM10 and TSP) was collected twice a week for two years (2011-2012) during working hours in Rourkela. The seasonal variations study of PM showed that the aerosol concentration was high during summer and low during monsoon. A detailed chemical characterization of both PM10 and TSP was carried out to find out the concentrations of different metal ions, anions and carbon content. The Spearman rank correlation analysis between different chemical species of PM depicted the presence of both crustal and anthropogenic origins in particulate matter. The enrichment factor analysis highlighted the presence of anthropogenic sources. Three major receptor models were used for the source apportionment of PM, namely chemical mass balance model (CMB), principal component analysis (PCA) and positive matrix factorization (PMF). In selecting source profiles for CMB, an effort has been put to select the profiles which represent the local conditions. Two of the profiles, namely soil dust and road dust, were developed in the present study for better accuracy. All three receptor models have shown that industrial (40-45%) and combustion sources (30-35%) were major contributors to particulate pollution in Rourkela. Artificial neural networks (ANN) were used for the prediction of particulate pollution using meteorological parameters as inputs. The emphasis is to compare the performances of MLP and RBF algorithms in forecasting and provide a rigorous inter-comparison as a first step toward operational PM forecasting models. The training, testing and validation errors of MLP networks are significantly lower than that of RBF networks. The results indicate that both MLP and RBF have shown good prediction capabilities while MLP networks were better than that of RBF networks. There is no profound bias that can be seen in the models which may also suggest that there are very few or zero external factors that may influence the dispersion and distribution of particulate matter in the study area

Particulate matter geochemistry of a highly industrialized region in the Caribbean: basis for future toxicological studies

Air pollution has become an important issue, especially in Caribbean urban areas, and, particulate matter (PM) emitted by different natural and anthropogenic sources causes environmental and health issues. In this work, we studied the concentrations of PM10 and PM2.5 sources in an industrial and port urban area in the Caribbean region of Colombia. PM samples were collected within 48-h periods between April and October 2018 by using a Partisol 2000i-D sampler. Elemental geochemical characterization was performed by X-ray fluorescence (XRF) analysis. Further, ionic species and black carbon (BC) were quantified by ion chromatography and reflectance spectroscopy, respectively. Using the Positive Matrix Factorization (PMF) receptor model, the contributions of PM sources were quantified. The average concentration of PM10 was 46.6 ± 16.2 μg/m3, with high concentrations of Cl and Ca. For PM2.5, the average concentration was 12.0 ± 3.2 μg/m3, and the most abundant components were BC, S, and Cl. The receptor model identified five sources for PM10 and PM2.5. For both fractions, the contributions of marine sea spray, re-suspended soil, and vehicular traffic were observed. In addition, PM2.5 included two mixed sources were found to be fuel oil combustion with fertilizer industry emissions, and secondary aerosol sources with building construction emissions. Further, PM10 was found to also include building construction emissions with re-suspended soil, and metallurgical industry emissions. These obtained geochemical atmospheric results are important for the implementation of strategies for the continuous improvement of the air quality of the Caribbean region

Characteristics of the pm10 in the urban environment of Makassar, Indonesia

Ambient PM10 samples were collected in Makassar, Province of South Sulawesi to examine the chemical characteristics of the airborne particulates in the area. The PM10 was monitored on a weekly basis for a period of one year from February 2012 to January 2013. A total of nineteen elemental constituents (i.e Ag, Al, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Si, Ti, and Zn) along with black carbon and ionic components SO4 2- > NO3-> Cl- > NH4 + were analyzed in the sample. The average PM10 concentration was found to be 32.92 µg/m3, lower than those found in other major cities of the world. The black carbon represents 6.1% of the PM10, the highest concentration, followed by SO4 2- (4.5%), NO3 - (3.4%), and Cl- (2.7%) while each of the elemental concentrations represent less than 2% of the PM10 at the site. The descending order of elemental concentration found at the site was Ca > Si > Na > Al > Fe > K > Mg > Zn > Ti > Pb > Ni >Mn> Ba > Cu > Cr > B > Ag > Cd > Co. The elemental enrichment factors indicated that most of these elements were enriched relative to soil origin illustrating their possible associations with other sources such as marine and anthropogenic derived aerosols. A better understanding of the potential air pollution sources in the city of Makassar was revealed in the study. © 2019 Journal of Urban and Environmental Engineering (JUEE)

Contribution of micro‐PIXE to the characterization of settled dust events in an urban area affected by industrial activities

This study aimed to identify possible sources of settled dust events that occurred in an urban area nearby an industrial park, which alarmed the local population. Settled dust was collected in January 2019 and its chemical characterization was assessed by micro-PIXE, focusing on a total of 29 elements. Comparison with chemical profiles of particulate matter from different types of environment was conducted, along with the assessment of crustal enrichment factors and Spearman correlations, allowing to understand which sources were contributing to this settled dust event. A nearby industrial area’s influence was identified due to the contents of Fe, Cr and Mn, which are typical tracers of iron and steel industries.info:eu-repo/semantics/publishedVersio

Response of organic aerosol to Delhi's pollution control measures over the period 2011–2018

Some of the world's highest air pollution episodes occur in Delhi, India and studies have shown particulate matter (PM) is the leading air pollutant to cause adverse health effects on Delhi's population. It is therefore vital to chart sources of PM over long time periods to effectively identify trends, particularly as multiple air quality mitigation measures have been implemented in Delhi over the past 10 years but remain unevaluated. An automated offline aerosol mass spectrometry (AMS) method has been developed which has enabled high-throughput analysis of PM filters. This novel offline-AMS method uses an organic solvent mix of acetone and water to deliver high extraction recoveries of organic aerosol (OA) (95.4 ± 8.3%). Positive matrix factorisation (PMF) source apportionment was performed on the OA fraction extracted from PM10 filter samples collected in Delhi in 2011, 2015 and 2018 to provide snapshots of the responses of OA to changes in sources in Delhi. The nine factors of OA resolved by PMF group into four primary source categories: traffic, cooking, coal-combustion and burning-related (solid fuel or open burning). Burning-related OA made the largest contribution during the winter and post-monsoon, when total OA concentrations were at their highest. Annual mean burning-related OA concentrations declined by 47% between 2015 and 2018, likely associated with the 2015 ban on open waste burning and controls and incentives to reduce crop-residue burning. Compositional analysis of OA factors shows municipal waste burning tracers still present in 2018, indicating further scope to reduce burning-related OA. The closure of the two coal power stations, along with initiatives to decrease coal use in industry, businesses, and residential homes, resulted in a significant decrease (87%) in coal-combustion OA. This corresponds to a 17% reduction in total OA, which shows the effectiveness of these measures in reducing PM10. Increases in traffic OA appear to have been offset by the introduction of the Bharat stage emissions standards for vehicles as the increases do not reflect the rapid increase in registered vehicles. However, daytime restrictions on heavy goods vehicles (HGVs) entering the city is linked to large increases in PM10 during the winter and post-monsoon, likely because the large influx of diesel-engine HGVs during the early mornings and evenings is timed with a particularly low planetary boundary layer height that enhances surface concentrations

The IAEA/RCA fine and coarse PMF receptor fingerprint database

This document accompanies the IAEA Master Positive Matrix Factorisation (PMF) Databases (fine and coarse). These databases have been generated from the 14 member state RCA Project RAC/07/015, “Characterization and Source Identification of Particulate Air Pollution in the Asia Region”. It fulfils the obligation under an IAEA contract to provide a fine and coarse ambient air PMF database with explanatory notes by February 2016 to the IAEA. The aim of this document is to provide instructional steps and related information necessary for navigating and utilising the IAEA Master Positive Matrix Factorisation (PMF) databases. It is important to note that interpretation of the PMF fingerprints and apportionment contained in either the coarse or fine databases are beyond the scope of this document. However, several countries have already published peer-reviewed papers related to their sites PMF source fingerprinting and source apportionment results. Comprehensive lists of publications are provided in Appendix 2