Distance impacts toxic metals pollution in mining affected river sediments.

The study of metals mobility derived from mining activities in an ultramafic lithology is limited. This study investigates the effects of distance on potentially toxic metals such as Co, Cu, Fe, Mn, Ni, Pb, and Zn pollution, and the geochemical processes of fluvial system downstream of an ex-copper mine (Mamut River). The toxicity level of the river was evaluated using various sediment quality guidelines, ecotoxicological risks (ecological risk and risk index) and pollution indices. The geochemical behavior and stability of these toxic metals in the solid-phase samples were also examined. The results show that elevated concentrations of Ni, Cu, and Fe in the sediments can be linked to the adsorption and precipitation of metals from the aqueous-phase samples. We found that the metal scavenging rate as a function of distance is more evident in tropical environments than it was previously thought (10 km downstream). Such an inference could be explained by the greater amount of rainfall (pH 5.5-6.5) received in the tropics and higher weathering products that could react and form stable complexes. Geochemical analysis of the river sediment indicates that Ni, Cu, and Fe in the river sediment have increased 44-, 81-, and 90-fold compared to the background values, respectively. A significant decrease in the concentration of the potentially toxic metals was found at 5.5 km downstream. The scavenging rate of Fe is the highest (1485.82 µg g-1 km-1) followed by Cu (141.48 µg g-1 km-1), Ni (10.23 µg g-1 km-1), Pb (8.12 µg g-1 km-1) and Zn (5.01 µg g-1 km-1) in the tropical river system. In contrast, the concentration of Co and Mn in the river sediments doubled as the river flows approximately 5 km downstream due to the higher mineral solubility and weaker metal partition coefficient. This study also discusses the possibility of asbestos (mainly as chrysotile in the X-ray diffraction) as a potential hidden risk present within the ultramafic setting. This case study can be extrapolated to explain the dispersion of inorganic pollutants in an ultramafic environment in a global context.

Spatial distribution of fine and coarse particulate matter during a southwest monsoon in Peninsular Malaysia.

This study aimed to determine the spatial distribution of PM2.5 and PM10 collected in four regions (North, Central, South and East Coast) of Peninsular Malaysia during the southwest monsoon. Concurrent measurements of PM2.5 and PM10 were performed using a high volume sampler (HVS) for 24 h (August to September 2018) collecting a total of 104 samples. All samples were then analysed for water soluble inorganic ions (WSII) using ion chromatography, trace metals using inductively coupled plasma-mass spectroscopy (ICP-MS) and polycyclic aromatic hydrocarbon (PAHs) using gas chromatography-mass spectroscopy (GC-MS). The results showed that the highest average PM2.5 concentration during the sampling campaign was in the North region (33.2 ± 5.3 µg m-3) while for PM10 the highest was in the Central region (38.6 ± 7.70 µg m-3). WSII recorded contributions of 22% for PM2.5 and 20% for PM10 mass, with SO42- the most abundant species with average concentrations of 1.83 ± 0.42 µg m-3 (PM2.5) and 2.19 ± 0.27 µg m-3 (PM10). Using a Positive Matrix Factorization (PMF) model, soil fertilizer (23%) was identified as the major source of PM2.5 while industrial activity (25%) was identified as the major source of PM10. Overall, the studied metals had hazard quotients (HQ) value of <1 indicating a very low risk of non-carcinogenic elements while the highest excess lifetime cancer risk (ELCR) was recorded for Cr VI in the South region with values of 8.4E-06 (PM2.5) and 6.6E-05 (PM10). The incremental lifetime cancer risk (ILCR) calculated from the PAH concentrations was within the acceptable range for all regions.

BTEX compositions and its potential health impacts in Malaysia.

This study aims to determine the composition of BTEX (benzene, toluene, ethylbenzene and xylene) and assess the risk to health at different sites in Malaysia. Continuous monitoring of BTEX in Kuala Lumpur City Centre, Kuala Terengganu, Kota Kinabalu and Fraser Hill were conducted using Online Gas Chromatograph. For comparison, BTEX at selected hotspot locations were determined by active sampling method using sorbent tubes and Thermal Desorption Gas Chromatography Mass Spectrometry. The hazard quotient (HQ) for non-carcinogenic and the life-time cancer risk (LTCR) of BTEX were calculated using the United States Environmental Protection Agency (USEPA) health risk assessment (HRA) methods. The results showed that the highest total BTEX concentrations using continuous monitoring were recorded in the Kuala Lumpur City Centre (49.56 ±â€¯23.71 µg/m3). Toluene was the most dominant among the BTEX compounds. The average concentrations of benzene ranged from 0.69 ±â€¯0.45 µg/m3 to 6.20 ±â€¯3.51 µg/m3. Measurements using active sampling showed that BTEX concentrations dominated at the roadside (193.11 ±â€¯114.57 µg/m3) in comparison to petrol station (73.08 ±â€¯30.41 µg/m3), petrochemical industry (32.10 ±â€¯13.13 µg/m3) and airport (25.30 ±â€¯6.17 µg/m3). Strong correlations among BTEX compounds (p<0.01, r>0.7) at Kuala Lumpur City Centre showed that BTEX compounds originated from similar sources. The values of HQ at all stations were <1 indicating the non-carcinogenic risk are negligible and do not pose threats to human health. The LTCR value based on benzene inhalation (1.59 × 10-5) at Kuala Lumpur City Centre were between 1 × 10-4 and 1 × 10-5, representing a probable carcinogenic risk.

Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System.

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur.

Long-term measurements (2004-2011) of PM10 (particulate matter with an aerodynamic diameter <10 µm) and trace gases (carbon monoxide [CO], ozone [O3], nitrogen oxide [NO], oxides of nitrogen [NO(x)], nitrogen dioxide [NO2], sulfur dioxide [SO2], methane [CH4], nonmethane hydrocarbon [NMHC]) have been conducted to study the effect of physicochemical factors on the PM10 concentration. In addition, this study includes source apportionment of PM10 in Kuala Lumpur urban environment. An advanced principal component analysis (PCA) technique coupled with absolute principal component scores (APCS) and multiple linear regression (MLR) has been applied. The average annual concentration of PM10 for 8 yr is 51.3 ± 25.8 µg m⁻³, which exceeds the Recommended Malaysian Air Quality Guideline (RMAQG) and international guideline values. Detail analysis shows the dependency of PM10 on the linear changes of the motor vehicles in use and the amount of biomass burning, particularly from Sumatra, Indonesia, during southwesterly monsoon. The main sources of PM10 identified by PCA-APCS-MLR are traffic combustion (28%), ozone coupled with meteorological factors (20%), and wind-blown particles (1%). However, the apportionment procedure left 28.0 µg m⁻³, that is, 51% of PM10 undetermined.