The Three Gorges Dam's construction and industrial transfer have resulted in a new air pollution pattern with the potential to threaten the reservoir eco-environment. To assess the impact of socioeconomic factors on the pattern of air quality vairation and economical risks, concentrations of SO2, NO2, and PM10, industry genres, and meteorological conditions were selected in the Three Gorges Reservoir of Chongqing (TGRC) during 2006-2015. Results showed that air quality had improved to some extent, but atmospheric NO2 showed an increased trend during 2011-2015. Spatially, higher atmospheric NO2 extended to the surrounding area. The primary industry, especially for agriculture, had shown to be responsible for the remarkable increase of atmospheric NO2 (p
The growing concern over the workers safety and health has lead many factories and organizations do the air monitoring to ensure the airborne at their workplace is safe for the worker’s health and complying the Occupational Safety and Health Act 1994 (Act 514). In this study, the monitoring covers an indoor air quality and chemical exposure to the workers in one of the power plant repair shop. A few workers from different sections namely blasting, welding, grinding, fitting and maintenance area were chosen to assist in the personal monitoring for 8 hours measurement. PM10 were measured at a few sampling points to collect dusts for 24 hours duration. The samples were brought back to the laboratory for gravimetric and SEM-EDAX analysis. The results were certainly exceed the limit for air quality, and many elements were detected such as Fe, Ni, Al, Si, Ca, K, Ba, S, Cr, Zn and Cl. The present of these elements shows that exposure to these particulate matters is quite risky and some measure needs to be taken for remedial action.
Meteorology over coastal region is a driving factor to the concentration of air particles and reactive gases. This study aims to conduct a research to determine the level of year-round air particles and the interaction of the meteorological driving factors with the particle number and mass in 2018, which is moderately influenced by Southeast Asian haze. We obtained the measurement data for particle number count (PNC), mass, reactive gases, and meteorological factors from a Global Atmospheric Watch (GAW) station located at Bachok Marine Research Center, Bachok, Kelantan, Malaysia. For various timeseries and correlation analyses, a 60-second resolution of the data has been averaged hourly and daily and visualized further. Our results showed the slight difference in particle behavior that is either measured by unit mass or number count at the study area. Diurnal variations showed that particles were generally high during morning and night periods. Spike was observed in August for PM2.5/PNC2.5 and PM10/PNC10 and in November for PMCoarse/PNCCoarse. From a polar plot, the particles came from two distinct sources (e.g., seaside and roadside) at the local scale. Regional wind vector shows two distinct wind-blown directions from northeast and southwest. The air mases were transported from northeast (e.g., Philippines, mainland China, and Taiwan) or southwest (e.g., Sumatra) region. Correlation analysis shows that relative humidity, wind direction, and pressure influence the increase in particles, whereas negative correlation with temperature is observed, and wind speed may have a potential role on the decline of particle concentration. The particles at the study area was highly influenced by the changes in regional wind direction and speed.
This research investigated the effects of co- and counter-current flow patterns on oil-water-solid separation efficiencies of a circular separator with inclined coalescence mediums. Oil-water-solid separations were tested at different influent concentrations and flowrates. Removal efficiencies increased as influent flowrate decreased, and their correlationship can be represented by power equations. These equations were used to predict the required flowrate, Q(ss50), for a given influent suspended solids concentration C(iss) to achieve the desired effluent suspended solids concentration, C(ess) of 50 mg/L, to meet environmental discharge requirements. The circular separator with counter-current flow was found to attend removal efficiencies relatively higher as compared to the co-current flow. As compared with co-current flow, counter-current flow Q(ss50) was approximately 1.65 times higher than co-current flow. It also recorded 13.16% higher oil removal at influent oil concentration, C(io) of 100 mg/L, and approximately 5.89% higher TSS removal at all influent flowrates. Counter-current flow's better removal performances were due to its higher coalescing area and constant interval between coalescence plate layers.
Results from the present study in Kuala Terengganu, Malaysia indicated a significant spatial variation but generally the total suspended particulate concentrations (mean = 17.2-148 microg/m(3)) recorded were below the recommended Malaysia guideline for total suspended particulate (mean of 24-h measurement = 260 microg/m(3)). Some of the elemental composition of particulate aerosol is clearly affected by non crustal sources, e.g. vehicular emission sources. Based on correlation and enrichment analyses, the elements could be grouped into two i.e. Pb, Cd and Zn group with sources from vehicular emission (r > 0.6; enrichment factor > 10) and Al, Fe, Mn and Cr group that appears to be of crustal origin (r > 0.6; enrichment factor < 10). It can also be concluded that the mean levels of Pb (1 ng/m(3)), Cd (0.02 ng/m(3)) and Zn (2 ng/m(3)) in the study area are generally lower than other urban areas in Malaysia (Pb < 181 ng/m(3); Cd < 6 ng/m(3); Zn < 192 ng/m(3)).
Air pollution has recently become a subject of increasing concern in many parts of the world. The World Health Organization (WHO) estimated that nearly 4.2 million early deaths are due to exposure to fine particles in polluted air, which causes multiple respiratory diseases. Algae, as a natural product, can be an alternative treatment due to potential biofunctional properties and advantages. This systematic review aims to summarize and evaluate the evidence of metabolites derived from algae as potential anti-inflammatory agents against respiratory disorders induced by atmospheric particulate matter (PM). Databases such as Scopus, Web of Science, and PubMed were systematically searched for relevant published full articles from 2016 to 2020. The main key search terms were limited to "algae", "anti-inflammation", and "air pollutant". The search activity resulted in the retrieval of a total of 36 publications. Nine publications are eligible for inclusion in this systematic review. A total of four brown algae (Ecklonia cava, Ishige okamurae, Sargassum binderi and Sargassum horneri) with phytosterol, polysaccharides and polyphenols were reported in the nine studies. The review sheds light on the pathways of particulate matter travelling into respiratory systems and causing inflammation, and on the mechanisms of actions of algae in inhibiting inflammation. Limitations and future directions are also discussed. More research is needed to investigate the potential of algae as anti-inflammatory agents against PM in in vivo and in vitro experimental models, as well as clinically.
Conventional fossil fuels are relied on heavily to meet the ever-increasing demand for energy required by human activities. However, their usage generates significant air pollutant emissions, such as NOx, SOx, and particulate matter. As a result, a complete air pollutant control system is necessary. However, the intensive operation of such systems is expected to cause deterioration and reduce their efficiency. Therefore, this study evaluates the current air pollutant control configuration of a coal-powered plant and proposes an upgraded system. Using a year-long dataset of air pollutants collected at 30-min intervals from the plant's telemonitoring system, untreated flue gas was reconstructed with a variational autoencoder. Subsequently, a superstructure model with various technology options for treating NOx, SOx, and particulate matter was developed. The most sustainable configuration, which included reburning, desulfurization with seawater, and dry electrostatic precipitator, was identified using an artificial intelligence (AI) model to meet economic, environmental, and reliability targets. Finally, the proposed system was evaluated using a Monte Carlo simulation to assess various scenarios with tightened discharge limits. The untreated flue gas was then evaluated using the most sustainable air pollutant control configuration, which demonstrated a total annual cost, environmental quality index, and reliability indices of 44.1 × 106 USD/year, 0.67, and 0.87, respectively.
At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.
The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
Polycyclic aromatic hydrocarbons (PAHs) are present in both gaseous and particulate phases. These compounds are considered to be atmospheric contaminants and are human carcinogens. Many studies have monitored atmospheric particulate and gaseous phases of PAH in Asia over the past 5 years. This work compares and discusses different sample collection, pretreatment and analytical methods. The main PAH sources are traffic exhausts (AcPy, FL, Flu, PA, Pyr, CHR, BeP) and industrial emissions (BaP, BaA, PER, BeP, COR, CYC). PAH concentrations are highest in areas of traffic, followed by the urban sites, and lowest in rural sites. Meteorological conditions, such as temperature, wind speed and humidity, strongly affect PAH concentrations at all sampling sites. This work elucidates the characteristics, sources and distribution, and the healthy impacts of atmospheric PAH species in Asia.
Petroleum sludge is a hazardous waste that contains various organic compounds including polycyclic aromatic hydrocarbons (PAHs) which have carcinogenic-mutagenic and toxic characteristics. This study focuses on the thermal treatment (indirect heating) of petroleum sludge cake for PAH degradation at 250, 450, and 650 degrees C using Ca(OH)2 + NaHCO3 as an additive. The treatment was conducted in a rotary drum electric heater. All experiments were carried out in triplicate. Concentrations of the 16 priority PAHs in gas (absorbed on Amberlite XAD-4 adsorbent), particulate (on quartz filter) and residue phases were determined using gas chromatography-mass spectrometry (GC-MS). The samples were extracted with acetonitrile by ultra-sonication prior to GC-MS analysis. The use of additive was beneficial and a temperature of 450 degrees C was suitable for PAH degradation. Low levels of PAH emissions, particularly carcinogenic PAH and toxic equivalent concentration (sigma TEC), were observed in gas, particulate and residue phases after treatment.
Air pollution has massive impacts on human life and poor air quality results in three million deaths annually. Air pollution can result from natural causes, including volcanic eruptions and extreme droughts, or human activities, including motor vehicle emissions, industry, and the burning of farmland and forests. Emission sources emit multiple pollutant types with diverse characteristics and impacts. However, there has been little research on the risk of multiple air pollutants; thus, it is difficult to identify multi-pollutant mitigation processes, particularly in Southeast Asia, where air pollution moves dynamically across national borders. In this study, the main objective was to develop a multi-air pollution risk index product for CO, NO2, and SO2 based on Sentinel-5P remote sensing data from 2019 to 2020. The risk index was developed by integrating hazard, vulnerability, and exposure analyses. Hazard analysis considers air pollution data from remote sensing, vulnerability analysis considers the air pollution sources, and exposure analysis considers the population density. The novelty of this study lies in its development of a multi-risk model that considers the weights obtained from the relationship between the hazard and vulnerability parameters. The highest air pollution risk index values were observed in urban areas, with a high exposure index that originates from pollution caused by human activity. Multi-risk analysis of the three air pollutants revealed that Singapore, Vietnam, and the Philippines had the largest percentages of high-risk areas, while Indonesia had the largest total high-risk area (4361 km2). Using the findings of this study, the patterns and characteristics of the risk distribution of multiple air pollutants in Southeast Asia can be identified, which can be used to mitigate multi-pollutant sources, particularly with respect to supporting the clean air targets in the Sustainable Development Goals.
Air pollution has been widely known to have an influence on health of the general population.
Air pollution can result from natural causes, human activities and transboundary air pollution.
Weather and climate play crucial role in determining the pattern of air quality. In recent years,
air pollution and recurrent episodes of haze has become a major concern in Malaysia.
Surveillance data on concentrations of main air pollutants such as carbon dioxide, (CO2),
Nitrogen Dioxide (NO2), Ozone (O3), sulphur dioxide (SO2) and particulate matter (PM10)
were found to be higher during the haze days and this may have an impact on health of the
community as reflected by an increase in hospital admissions particularly the respiratory and
cardiovascular diseases.
The concentrations of airborne particulate matter (PM) is often measured as a mass concentration. However, the other way to express particulate matter is by using the Particle Number Count ([PNC]) concentrations. This study aims to analyse the seasonal variation of airborne particulate matter in terms of [PNC] by using R packages and the Boosted Regression Trees (BRTs) technique. The study was conducted at IOES, Universiti of Malaya in Bachok, Kelantan. The monitoring was important to understand the variability of seasonal effects due to different seasons. In this work, only the datasets for three seasons (Inter Monsoon, North East Monsoon and South-West Monsoon) were analysed involving 25,958 data. The air quality monitoring equipment involved was the particle counter Environment Dust Monitor GRIMM Model 180 and a weather station for recording the meteorological parameters. The data analysis was completed by using R software and its package for evaluating seasonal variability and providing the statistical analysis. The relationship between variables was studied by using the Boosted Regression Tree (BRT) technique. The interaction between independent variables towards the [PNC] in different seasons was discussed. The best setting result of BRT model evaluation R² is 0.22 (North-East Monsoon), 0.87 (Intern monsoon 1), and 0.59 for South West Monsoon which indicated that the model developed is acceptable except for NEM and intern monsoon seasons. Temperature (57 %) and wind direction (67%) were found to be the highest factor influenced by the formation of [PNC] concentrations in this area. Finally, good results indicated that BRT technique is an acceptable way to analysed air pollution data.
Particulate matter (PM) air pollution is a human lung carcinogen; however, the components responsible have not been identified. We assessed the associations between PM components and lung cancer incidence.
Southeast Asia experiences frequent fires in fuel-rich tropical peatlands, leading to extreme episodes of regional haze with high concentrations of fine particulate matter (PM2.5) impacting human health. In a study published recently, the first field measurements of PM2.5 emission factors for tropical peat fires showed larger emissions than from other fuel types. Here we report even higher PM2.5 emission factors, measured at newly ignited peat fires in Malaysia, suggesting that current estimates of fine particulate emissions from peat fires may be underestimated by a factor of 3 or more. In addition, we use both field and laboratory measurements of burning peat to provide the first mechanistic explanation for the high variability in PM2.5 emission factors, demonstrating that buildup of a surface ash layer causes the emissions of PM2.5 to decrease as the peat fire progresses. This finding implies that peat fires are more hazardous (in terms of aerosol emissions) when first ignited than when still burning many days later. Varying emission factors for PM2.5 also have implications for our ability to correctly model the climate and air quality impacts downwind of the peat fires. For modelers able to implement a time-varying emission factor, we recommend an emission factor for PM2.5 from newly ignited tropical peat fires of 58 g of PM2.5 per kilogram of dry fuel consumed (g/kg), reducing exponentially at a rate of 9%/day. If the age of the fire is unknown or only a single value may be used, we recommend an average value of 24 g/kg.
In Asian countries such as China, Malaysia, Pakistan, India, Taiwan, Korea, Japan and Hong Kong, ambient air total suspended particulates and PM2.5 concentration data were collected and discussed during the years of 1998-2015 in this study. The aim of the present study was to (1) investigate and collect ambient air total suspended particulates (TSP) and PM2.5 concentrations for Asian countries during the past two decades. (2) Discuss, analyze and compare those particulates (TSP and PM2.5) annual concentration distribution trends among those Asian countries during the past two decades. (3) Test the mean concentration differences in TSP and PM2.5 among the Asian countries during the past decades. The results indicated that the mean TSP concentration order was shown as China > Malaysia > Pakistan > India > Taiwan > Korea > Japan. In addition, the mean PM2.5 concentration order was shown as Vietnam > India > China > Hong Kong > Mongolia > Korea > Taiwan > Japan and the average percentages of PM2.5 concentrations for Taiwan, China, Japan, Korea, Hong Kong, Mongolia and Other (India and Vietnam) were 8, 21, 6, 8, 14, 13 and 30%, respectively, during the past two decades. Moreover, t test results revealed that there were significant mean TSP and PM2.5 concentration differences for either China or India to any of the countries such as Taiwan, Korea and Japan in Asia during the past two decades for this study. Noteworthy, China and India are both occupied more than 60% of the TSP and PM2.5 particulates concentrations out of all the Asia countries. As for Taiwan, the average PM2.5 concentration displayed increasing trend in the years of 1998-1999. However, it showed decreasing trend in the years of 2000-2010. As for Korea, the average PM2.5 concentrations showed decreasing trend during the years of 2001-2013. Finally, the average PM2.5 concentrations for Mongolia displayed increasing trend in the years of 2004-2013.
This study aimed to determine the distribution and potential health risks of polycyclic aromatic hydrocarbons (PAHs) in PM2.5 collected in Kuala Lumpur during different monsoon seasons. The potential sources of PM2.5 were investigated using 16 priority PAHs with additional of biomass tracers namely levoglucosan (LV), mannosan (MN) and galactosan (GL). This study also investigated the cytotoxic potential of the extracted PAHs towards V79-4 cells. A high-volume air sampler (HVS) was used to collect PM2.5 samples for 24 h. PAHs were extracted using dichloromethane (DCM) while biomass tracers were extracted by a mixture of DCM/methanol (3:1) before analysis with gas chromatography-mass spectrometry (GC-MS). The cytotoxicity of the PAHs extract was determined by assessing the cell viability through the reduction of tetrazolium salts (MTT). The results showed that the total mean ± SD concentrations of PAHs during the southwest (SW) and northeast (NE) monsoons were 2.51 ± 0.93 ng m-3 and 1.37 ± 0.09 ng m-3, respectively. Positive matrix factorization (PMF) using PAH and biomass tracer concentrations suggested four potential sources of PM2.5; gasoline emissions (29.1%), natural gas and coal burning (28.3%), biomass burning (22.3%), and diesel and heavy oil combustion (20.3%). Health risk assessment showed insignificant incremental lifetime cancer risk (ILCR) of 2.40E-07 for 70 years of exposure. MTT assay suggested that PAHs extracts collected during SW monsoon have cytotoxic effect towards V79-4 cell at the concentrations of 25 μg mL-1, 50 μg mL-1, 100 μg mL-1 whereas non-cytotoxic effect was observed on the PAHs sample collected during NE monsoon.
The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.