Air Pollution Associated with Total Suspended Particulate and Particulate Matter in Cement Grinding Plant in Vietnam

Air pollution associated with suspended particles has become a significant concern in Vietnam recently. The study aimed to (1) investigate dust sources; (2) measure concentration levels of Total Suspended Particulate (TSP), Particulate Matter (PM) fractions; (3) identify silica levels and the correlation with respirable particles at a cement grinding plant in Vietnam. A total of 312 samples (52 TSP, 160 PMs) at 13 processes were measured using the direct-reading dust meter. The silica composition was analyzed in a certified laboratory using the X-ray fluorescence (XRF) technique. SPSS version 26 for Window was used to analyze the data. The operations of the cement grinding plant created multiple dust sources from the jetty to the cement dispatch process. The TSP levels ranged 0.06–38.24 mg m−3, and 40.38 % (n = 21) TSP samples exceeded the Permissible Exposure Limit (PEL) for an 8-h working shift. Besides that, there was a wide range and significant concentration levels of PMs in the cement processes. The levels of PMs were PM1 (0.00–0.06 mg m−3), PM2.5 (0.01–0.83 mg m−3), PM4 (0.02–4.59 mg m−3), PM7 (0.03–16.94 mg m−3), and PM10 (0.04–26.85 mg m−3). The highest mean levels of PMs factions were measured at the pre-grinding process. The inefficient operation of the dust collector contributed a significant factor to the dust dispersion in this process. The silica’s mean (SD) composition in respirable dust was 20.4 % (0.86) and was not significantly different amongst the processes. There was a significant correlation between the levels of respirable dust and silica exposure in the cement grinding plant (r = 0.99). The improvement of indoor air quality is needed to prevent health effects on cement workers.

Karakteristik Kimia dan Morfologi dari Total Suspended Particulate (TSP) di Jakarta dan Puncak-Bogor pada Masa Pembatasan Sosial Berskala Besar

Penggunaan transportasi umum di DKI Jakarta selama pemberlakuan Pembatasan Sosial Berskala Besar (PSBB) periode April – Mei 2020 meningkatkan kualitas udara secara signifikan, dibandingkan dengan tahun 2019. Salah satu parameter yang dapat menentukan kualitas udara adalah Total Suspended Particulate (TSP). Sampel TSP dikumpulkan dari lokasi Jaringan Pemantau Kualitas Udara Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) di Stasiun Meteorologi Kemayoran-Jakarta dan Pos Polusi Udara Cibeureum, Puncak-Bogor menggunakan alat High Volume Air Sampler (HVAS) selama 24 jam. Periode pengambilan sampel setiap enam hari sekali mulai 14 Maret hingga 19 Mei 2020. Konsentrasi TSP ditentukan menggunakan metode gravimetri. Rata-rata konsentrasi TSP pada tiga periode sampling pertama April 2020 (menjelang dan awal berlakunya PSBB) memiliki nilai terendah di Jakarta dan Puncak-Bogor berturut-turut sebesar 80,08 mg/m3 dan 40,51 mg/m3. Tingkat potensi toksisitas ditentukan untuk mengetahui efeknya terhadap kesehatan manusia. Potensi toksisitas dihitung dengan membagi konsentrasi TSP dengan nilai baku mutu nasional sebesar 230 ug/m3. Nilai potensial toksisitas rata-rata di Jakarta dan Puncak-Bogor masing-masing sebesar 0,527 dan 0,220. Sumber asal materi partikulat diketahui dengan digunakan model pollution-rose. Sampel TSP dikarakterisasi menggunakan instrumen Scanning Electron Microscopy (SEM). Unsur yang melimpah pada permukaan partikel, secara berurutan terdiri dari O, Si, C, Na, Al, K dan Ca. Rasio komponen (Ca, C, O, Na, Al, Si, dan K) yang terdapat pada sampel TSP dari Jakarta dan Puncak-Bogor masing-masing sebesar 1,303; 1,060; 1,026; 0,995; 0,969; 0,898; dan 0,882. TSP dari Puncak-Bogor memiliki morfologi dengan bentuk cenderung tidak beraturan, sedangkan TSP dari Jakarta cenderung berbentuk bulat yang bertumpuk. Berdasarkan morfologi dan analisis kimianya, sebagian besar sumber TSP di Puncak-Bogor berasal dari alam, sedangkan TSP di Jakarta berasal dari campuran partikulat sumber antropogenik.

Chemical Composition and Source Apportionment of Total Suspended Particulate in the Central Himalayan Region

The present study analyzes data from total suspended particulate (TSP) samples collected during 3 years (2005–2008) at Nainital, central Himalayas, India and analyzed for carbonaceous aerosols (organic carbon (OC) and elemental carbon (EC)) and inorganic species, focusing on the assessment of primary and secondary organic carbon contributions (POC, SOC, respectively) and on source apportionment by positive matrix factorization (PMF). An average TSP concentration of 69.6 ± 51.8 µg m−3 was found, exhibiting a pre-monsoon (March–May) maximum (92.9 ± 48.5 µg m−3) due to dust transport and forest fires and a monsoon (June–August) minimum due to atmospheric washout, while carbonaceous aerosols and inorganic species expressed a similar seasonality. The mean OC/EC ratio (8.0 ± 3.3) and the good correlations between OC, EC, and nss-K+ suggested that biomass burning (BB) was one of the major contributing factors to aerosols in Nainital. Using the EC tracer method, along with several approaches for the determination of the (OC/EC)pri ratio, the estimated SOC component accounted for ~25% (19.3–29.7%). Furthermore, TSP source apportionment via PMF allowed for a better understanding of the aerosol sources in the Central Himalayan region. The key aerosol sources over Nainital were BB (27%), secondary sulfate (20%), secondary nitrate (9%), mineral dust (34%), and long-range transported mixed marine aerosol (10%). The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses were also used to identify the probable regional source areas of resolved aerosol sources. The main source regions for aerosols in Nainital were the plains in northwest India and Pakistan, polluted cities like Delhi, the Thar Desert, and the Arabian Sea area. The outcomes of the present study are expected to elucidate the atmospheric chemistry, emission source origins, and transport pathways of aerosols over the central Himalayan region.

Gridded distribution of total suspended particulate matter (TSP) and their chemical characterization over Delhi during winter

In the present study, total suspended particulate matter (TSP) samples were collected at 47 different sites (47 grids of 5 × 5 km2 area) of Delhi during winter (January-February 2019) in campaign mode. To understand the spatial variation of sources, TSP samples were analyzed for chemical compositions including carbonaceous species [organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)], water-soluble total nitrogen (WSTN), water-soluble inorganic nitrogen (WSIN), polycyclic aromatic hydrocarbons (16 PAHs), water-soluble inorganic species (WSIS) (F−, Cl−, SO42−, NO2−, NO3−, PO43−, NH4+, Ca2+, Mg2+, Na+, and K+), and major & minor trace elements (B, Na, Mg, Al, P, S, Cl, K, Ca, Ti, Fe, Zn, Cr, Mn, Cu, As, Pd, F, and Ag). During the campaign, the maximum concentration of several components of TSP (996 µg/m3) was recorded at the Rana Pratap Bagh area representing a pollution hotspot of Delhi. The maximum concentrations of PAHs were recorded at Udhyog Nagar, a region close to heavily loaded diesel vehicles, small rubber factories, and waste burning areas. Higher content of Cl− and Cl−/Na+ ratio (> 1.7) suggests the presence of nonmarine anthropogenic sources of Cl− over Delhi. Minimum concentrations of OC, EC, WSOC, PAHs, and WSIS in TSP were observed at Kalkaji representing the least polluted area in Delhi. Enrichment factor < 5.0 at several locations and a significant correlation of Al with Mg, Fe, Ti, and Ca and C/N ratio indicated the abundance of mineral/crustal dust in TSP over Delhi. Principal component analysis (PCA) was also performed for the source apportionment of TSP and extracted soil dust was found to be the major contributor to TSP followed by biomass burning, open waste burning, secondary aerosol, and vehicular emissions.

Analisis Risiko Kesehatan Lingkungan Pajanan Debu Kayu pada Pekerja Mebel Informal di Kelurahan Antang Kecamatan Manggala Kota Makassar

Mebel Informal di Kelurahan Antang Kecamatan Manggala Kota Makassar adalah industri berskala rumah tangga (home industry) yang mengolah kayu menjadi produk furniture. Proses pengolahannya cenderung menghasilkan debu kayu yang dicurigai berpotensi menimbulkan kontaminasi polusi udara termasuk debu atau Total Suspended Particulate (TSP) di tempat kerja serta gangguan kesehatan pada pekerja mebel. Penelitian ini bertujuan untuk mengkaji risiko kesehatan lingkungan dari pajanan debu kayu pada pekerja di Mebel Informal Kelurahan Antang. Jenis penelitian yang digunakan adalah kuantitatif dengan desain studi Analisis Risiko Kesehatan Lingkungan (ARKL). Populasi dalam penelitian ini adalah seluruh pekerja di Mebel Informal UD. Haming dan UD. Pondok Mekar yang berjumlah 30 orang dengan menggunakan teknik total sampling. Teknik pengambilan data dengan menggunakan kuesioner dan wawancara serta melakukan pengukuran dengan alat. Data dianalisis dengan menggunakan analisis univariat dan Analisis Risiko Kesehatan Lingkungan (ARKL). Hasil penelitian menunjukkan bahwa rata-rata konsentrasi TSP di lokasi Mebel Informal telah melebihi Nilai Ambang Batas (NAB), serta berdasarkan perhitungan ARKL menunjukkan seluruh asupan (intake) pekerja baik realtime maupun lifetime masih berada di bawah dosis referensi dan estimasi besaran risiko pekerja terpajan TSP adalah RQ<1 yang artinya belum terjadi risiko pajanan TSP pada pekerja saat ini hingga beberapa tahun mendatang. Kata kunci : Konsentrasi TSP; ARKL; pekerja mebel informal Mebel Informal di Kelurahan Antang Kecamatan Manggala Kota Makassar adalah industri berskala rumah tangga (home industry) yang mengolah kayu menjadi produk furniture. Proses pengolahannya cenderung menghasilkan debu kayu yang dicurigai berpotensi menimbulkan kontaminasi polusi udara termasuk debu atau Total Suspended Particulate (TSP) di tempat kerja serta gangguan kesehatan pada pekerja mebel. Penelitian ini bertujuan untuk mengkaji risiko kesehatan lingkungan dari pajanan debu kayu pada pekerja di Mebel Informal Kelurahan Antang. Jenis penelitian yang digunakan adalah kuantitatif dengan desain studi Analisis Risiko Kesehatan Lingkungan (ARKL). Populasi dalam penelitian ini adalah seluruh pekerja di Mebel Informal UD. Haming dan UD. Pondok Mekar yang berjumlah 30 orang dengan menggunakan teknik total sampling. Teknik pengambilan data dengan menggunakan kuesioner dan wawancara serta melakukan pengukuran dengan alat. Data dianalisis dengan menggunakan analisis univariat dan Analisis Risiko Kesehatan Lingkungan (ARKL). Hasil penelitian menunjukkan bahwa rata-rata konsentrasi TSP di lokasi Mebel Informal telah melebihi Nilai Ambang Batas (NAB), serta berdasarkan perhitungan ARKL menunjukkan seluruh asupan (intake) pekerja baik realtime maupun lifetime masih berada di bawah dosis referensi dan estimasi besaran risiko pekerja terpajan TSP adalah RQ<1 yang artinya belum terjadi risiko pajanan TSP pada pekerja saat ini hingga beberapa tahun mendatang.

Emission of Polycyclic Aromatic Hydrocarbons in Smoke Particulates at Three Different Combustion Stages from Burning of Rhizophora apiculate, Melaleuca leucadendron and Hevea brasiliensis Wood

The purpose of this study was to characterize and determine the concentrations of polycyclic aromatic hydrocarbons (PAHs) emitted in smoke particulates from burning of Rhizophora apiculata, Melaleuca leucadendron and Hevea Brasilensis at the smouldering, flaming and charring stages. Smoke particulates were sampled using a total suspended particulate Hi-volume sampler (HVS) at a rate of 1.13 m3/min and PAHs were extracted with a mixture of dichloromethane-methanol (3:1 v/v) using ultrasonic agitation. Fractionation of PAHs was carried out on an alumina-silica column and analysis by gas chromatography-mass spectrometry (GC-MS). The results showed that most of the samples exhibited the highest total identified PAHs in the smouldering stage with formation of PAHs with three rings or more increasing from the smouldering to flaming stages and reducing as combustion entered the charring stage. Naphthalene, phenanthrene and pyrene were the dominant PAHs detected in the wood smoke particulates, depending on combustion stage. Overall the emission and formation of PAHs are strongly dependent on combustion stage as well as other factors such as wood morphology, species, moisture content and combustion temperature.

Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters

In order to use in situ sensed reflectance to monitor the concentrations of chlorophyll-a (Chl-a) and total suspended particulate (TSP) of waters in the Pearl River Delta, which is featured by the highly developed network of rivers, channels and ponds, 135 sets of simultaneously collected water samples and reflectance were used to test the performance of the traditional empirical models (band ratio, three bands) and the machine learning models of a back-propagation neural network (BPNN). The results of the laboratory analysis with the water samples show that the Chl-a ranges from 3 to 256 µg·L−1 with an average of 39 µg·L−1 while the TSP ranges from 8 to 162 mg·L−1 and averages 42.5 mg·L−1. Ninety sets of 135 samples are used as training data to develop the retrieval models, and the remaining ones are used to validate the models. The results show that the proposed band ratio models, the three-band combination models, and the corresponding BPNN models are generally successful in estimating the Chl-a and the TSP, and the mean relative error (MRE) can be lower than 30% and 25%, respectively. However, the BPNN models have no better performance than the traditional empirical models, e.g., in the estimation of TSP on the basis of the reflectance at 555 and 750 nm (R555 and R750, respectively), the model of BPNN (R555, R750) has an MRE of 23.91%, larger than that of the R750/R555 model. These results suggest that these traditional empirical models are usable in monitoring the optically active water quality parameters of Chl-a and TSP for eutrophic and turbid waters, while the machine learning models have no significant advantages, especially when the cost of training samples is considered. To improve the performance of machine learning models in future applications on the basis of ground sensor networks, large datasets covering various water situations and optimization of input variables of band configuration should be strengthened.