The aim of this study is to examine the impact of air pollutants, including mono-nitrogen oxides (NOx), nitrous oxide (N2O), sulfur dioxide (SO2), carbon dioxide emissions (CO2), and greenhouse gas (GHG) emissions on ecological footprint, habitat area, food supply, and biodiversity in a panel of thirty-four developed and developing countries, over the period of 1995-2014. The results reveal that NOx and SO2 emissions both have a negative relationship with ecological footprints, while N2O emission and real GDP per capita have a direct relationship with ecological footprints. NOx has a positive relationship with forest area, per capita food supply and biological diversity while CO2 emission and GHG emission have a negative impact on food production. N2O has a positive impact on forest area and biodiversity, while SO2 emissions have a negative relationship with them. SO2 emission has a direct relationship with per capita food production, while GDP per capita significantly affected per capita food production and food supply variability across countries. The overall results reveal that SO2, CO2, and GHG emissions affected potential habitat area, while SO2 and GHG emissions affected the biodiversity index. Trade liberalization policies considerably affected the potential habitat area and biological diversity in a panel of countries.
The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.
Matched MeSH terms: Air Pollutants*; Air Pollution*
Nineteen pairs of gaseous and surface seawater samples were collected along the cruise from Malaysia to the south of Bay of Bengal passing by Sri Lanka between April 12 and May 4, 2011 on the Chinese research vessel Shiyan I to investigate the latest OCP pollution status over the equatorial Indian Ocean. Significant decrease of α-HCH and γ-HCH was found in the air and dissolved water phase owing to global restriction for decades. Substantially high levels of p,p'-DDT, o,p'-DDT, trans-chlordane (TC), and cis-chlordane (CC) were observed in the water samples collected near Sri Lanka, indicating fresh continental riverine input of these compounds. Fugacity fractions suggest equilibrium of α-HCH at most sampling sites, while net volatilization for DDT isomers, TC and CC in most cases. Enantiomer fractions (EFs) of α-HCH and o,p'-DDT in the air and water samples were determined to trace the source of these compounds in the air. Racemic or close to racemic composition was found for atmospheric α-HCH and o,p'-DDT, while significant depletion of (+) enantiomer was found in the water phase, especially for o,p'-DDT (EFs = 0.310 ± 0.178). 24% of α-HCH in the lower air over the open sea of the equatorial Indian Ocean is estimated to be volatilized from local seawater, indicating that long-range transport is the main source.
The problems of global warming and the unstable price of petroleum oils have led to a race to develop environmentally friendly biofuels, such as palm oil or ethanol derived from corn and sugar cane. Biofuels are a potential replacement for fossil fuel, since they are renewable and environmentally friendly. This paper evaluates the combustion performance and emission characteristics of Refined, Bleached, and Deodorized Palm Oil (RBDPO)/diesel blends B5, B10, B15, B20, and B25 by volume, using an industrial oil burner with and without secondary air. Wall temperature profiles along the combustion chamber axis were measured using a series of thermocouples fitted axially on the combustion chamber wall, and emissions released were measured using a gas analyzer. The results show that RBDPO blend B25 produced the maximum emission reduction of 56.9% of CO, 74.7% of NOx, 68.5% of SO(2), and 77.5% of UHC compared to petroleum diesel, while air staging (secondary air) in most cases reduces the emissions further. However, increasing concentrations of RBDPO in the blends also reduced the energy released from the combustion. The maximum wall temperature reduction was 62.7% for B25 at the exit of the combustion chamber.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/prevention & control
Siliceous materials such as rice husk ash (RHA) have potential to be utilized as high performance sorbents for the flue gas desulfurization process in small-scale industrial boilers. This study presents findings on identifying the key factorfor high desulfurization activity in sorbents prepared from RHA. Initially, a systematic approach using central composite rotatable design was used to develop a mathematical model that correlates the sorbent preparation variables to the desulfurization activity of the sorbent. The sorbent preparation variables studied are hydration period, x1 (6-16 h), amount of RHA, x2 (5-15 g), amount of CaO, x3 (2-6 g), amount of water, x4 (90-110 mL), and hydration temperature, x5 (150-250 degrees C). The mathematical model developed was subjected to statistical tests and the model is adequate for predicting the SO2 desulfurization activity of the sorbent within the range of the sorbent preparation variables studied. Based on the model, the amount of RHA, amount of CaO, and hydration period used in the preparation step significantly influenced the desulfurization activity of the sorbent. The ratio of RHA and CaO used in the preparation mixture was also a significant factor that influenced the desulfurization activity of the sorbent. A RHA to CaO ratio of 2.5 leads to the formation of specific reactive species in the sorbent that are believed to be the key factor responsible for high desulfurization activity in the sorbent. Other physical properties of the sorbent such as pore size distribution and surface morphology were found to have insignificant influence on the desulfurization activity of the sorbent.
Matched MeSH terms: Air Pollutants/isolation & purification*; Air Pollutants/chemistry; Air Pollution/prevention & control*
The objective of this research was to investigate the relationship between lung cancer mortality rates, carcinogenic polycyclic aromatic hydrocarbon (PAH) emissions, and smoking on a global scale, as well as for different socioeconomic country groups. The estimated lung cancer deaths per 100,000 people (ED100000) and age standardized lung cancer death rate per 100,000 people (ASDR100000) in 2004 were regressed on PAH emissions in benzo[a]pyrene equivalence (BaPeq), smoking prevalence, cigarette price, gross domestic product per capita, percentage of people with diabetes, and average body mass index using simple and multiple linear regression for 136 countries. Using stepwise multiple linear regression, a statistically significant positive linear relationship was found between loge(ED100000) and loge(BaPeq) emissions for high (p-value <0.01) and for the combination of upper-middle and high (p-value <0.05) socioeconomic country groups. A similar relationship was found between loge(ASDR100000) and loge(BaPeq) emissions for the combination of upper-middle and high (p-value <0.01) socioeconomic country groups. Conversely, for loge(ED100000) and loge(ASDR100000), smoking prevalence was the only significant independent variable in the low socioeconomic country group (p-value <0.001). These results suggest that reducing BaPeq emissions in the U.S., Canada, Australia, France, Germany, Brazil, South Africa, Poland, Mexico, and Malaysia could reduce ED100000, while reducing smoking prevalence in Democratic People's Republic of Korea, Nepal, Mongolia, Cambodia, and Bangladesh could significantly reduce the ED100000 and ASDR100000.
The occurrence of n-alkanoic acids, amides, and nitriles in samples of aerosol particulate matter from Kuala Lumpur and Santiago suggests that emissions from cooking and biomass burning are the primary sources of these organic markers in the atmosphere. It is proposed that fatty acids react with ammonia during biomass burning or combustion to produce amides and nitriles, which can be applied as useful biomarker tracers. To test this hypothesis, nonadecanoic acid and hexadecanamide were used as reactants in hydrous pyrolysis experiments. These experiments produced amides and nitriles and indicated that ammonia is an essential agent in their formation. Thus amides and nitriles are of utility as indicators for input from combustion and biomass burning in the ambient atmosphere.
An integrated source apportionment methodology is developed by amalgamating the receptor-oriented model (ROM) and source-oriented numerical simulations (SOM) together to eliminate the weaknesses of individual SA methods. This approach attempts to apportion and dissect the PM2.5 sources in the Yangtze River Delta region during winter. First, three ROM models (CMB, PMF, ME2) are applied and compared for the preliminary SA results, with information from PM2.5 sampling and lab analysis during the winter seasons. The detailed source category contribution of SOM to PM2.5 is further simulated using the WRF-CAMx model. The two pieces of information from both ROM and SOM are then stitched together to give a comprehensive information on the PM2.5 sources over the region. With the integrated approach, the detailed contributing sources of the ambient PM2.5 at different receptors including rural and urban, coastal and in-land, northern and southern receptors are analyzed. The results are compared with previous data and shows good agreement. This integrative approach is more comprehensive and is able to produce a more profound and detailed understanding between the sources and receptors, compared with single models.
Recent findings of an unexpected slowdown in the decline of CFC-11 mixing ratios in the atmosphere have led to the conclusion that global CFC-11 emissions have increased over the past decade and have been attributed in part to eastern China. This study independently assesses these findings by evaluating enhancements of CFC-11 mixing ratios in air samples collected in Taiwan between 2014 and 2018. Using the NAME (Numerical Atmospheric Modeling Environment) particle dispersion model, we find the likely source of the enhanced CFC-11 observed in Taiwan to be East China. Other halogenated trace gases were also measured, and there were positive interspecies correlations between CFC-11 and CHCl3, CCl4, HCFC-141b, HCFC-142b, CH2Cl2, and HCFC-22, indicating co-location of the emissions of these compounds. These correlations in combination with published emission estimates of CH2Cl2 and HCFC-22 from China, and of CHCl3 and CCl4 from eastern China, are used to estimate CFC-11 emissions. Within the uncertainties, these estimates do not differ for eastern China and the whole of China, so we combine them to derive a mean estimate that we term as being from "(eastern) China". For 2014-2018, we estimate an emission of 19 ± 5 Gg year-1 (gigagrams per year) of CFC-11 from (eastern) China, approximately one-quarter of global emissions. Comparing this to previously reported CFC-11 emissions estimated for earlier years, we estimate CFC-11 emissions from (eastern) China to have increased by 7 ± 5 Gg year-1 from the 2008-2011 average to the 2014-2018 average, which is 50 ± 40% of the estimated increase in global CFC-11 emissions and is consistent with the emission increases attributed to this region in an earlier study.
Open biomass burning in Peninsula Malaysia, Sumatra, and parts of the Indochinese region is a major source of transboundary haze pollution in the Southeast Asia. To study the influence of haze on rainwater chemistry, a short-term investigation was carried out during the occurrence of a severe haze episode from March to April 2014. Rainwater samples were collected after a prolonged drought and analyzed for heavy metals and major ion concentrations using inductively coupled plasma mass spectroscopy (ICP-MS) and ion chromatography (IC), respectively. The chemical composition and morphology of the solid particulates suspended in rainwater were examined using a scanning electron microscope coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). The dataset was further interpreted using enrichment factors (EF), statistical analysis, and a back trajectory (BT) model to find the possible sources of the particulates and pollutants. The results show a drop in rainwater pH from near neutral (pH 6.54) to acidic (
Matched MeSH terms: Air Movements; Air Pollutants*
This study emphasizes on near surface observation of chemically active trace gases such as nitrogen dioxide (NO2) over Islamabad on a regular basis. Absorption spectroscopy using backscattered extraterrestrial light source technique was used to retrieve NO2 differential slant column densities (dSCDs). Mini multi-axis-differential optical absorption spectroscopy (MAX-DOAS) instrument was used to perform ground-based measurements at Institute of Environmental Sciences and Engineering (IESE), National University of Sciences and Technology (NUST) Islamabad, Pakistan. Tropospheric vertical column densities (VCDs) of NO2 were derived from measured dSCDs by using geometric air mass factor approach. A case study was conducted to identify the impact of different materials (glass, tinted glass, and acrylic sheet of various thicknesses used to cover the instrument) on the retrieval of dSCDs. Acrylic sheet of thickness 5 mm was found most viable option for casing material as it exhibited negligible impact in the visible wavelength range. Tropospheric NO2 VCD derived from ground-based mini MAX-DOAS measurements exceeded two times the Pak-NEQS levels and showed a reasonable comparison (r (2) = 0.65, r = 0.81) with satellite observations (root mean square bias of 39 %) over Islamabad, Pakistan.
In this study, palm oil mill sludge was used as a precursor to prepare biochar using conventional pyrolysis. Palm oil mill sludge biochar (POSB) was prepared at different preparation variables, i.e., heating temperature (300-800 °C), heating rate (10-20 °C/min) and holding time (60-120 min). The prepared biochars were tested for sulfur dioxide (SO2) adsorption in a fixed bed reactor using 300 ppm of SO2 gas at 300 ml/min (with N2 gas as balance). Response surface central composite experimental design was used to optimize the production of biochar versus SO2 removal. A quadratic model was developed in order to correlate the effect of variable parameters on the optimum adsorption capacity of SO2 gas. The experimental values and the predicted results of the model were found to show satisfactory agreement. The optimum conditions for biochar preparation to yield the best SO2 removal was found to be at 405 °C of heating temperature, 20 °C/min of heating rate and 88 min of holding time. At these conditions, the average yield of biochar and adsorption capacity for SO2 gas was reported as 54.25 g and 9.75 mg/g, respectively. The structure of biochar and their roles in SO2 adsorption were investigated by surface area, morphology images, infrared spectra, and proximate analysis, respectively. The characterization findings suggested that POSB adsorbs SO2 mainly by the functional groups.
This study aimed to determine bioavailable heavy metal concentrations (As, Cd, Co, Cu, Cr, Ni, Pb, Zn) and their potential sources in classroom dust collected from children's hand palms in Rawang (Malaysia). This study also aimed to determine the association between bioavailable heavy metal concentration in classroom dust and children's respiratory symptoms. Health risk assessment (HRA) was applied to evaluate health risks (non-carcinogenic and carcinogenic) due to heavy metals in classroom dust. The mean of bioavailable heavy metal concentrations in classroom dust found on children's hand palms was shown in the following order: Zn (1.25E + 01 μg/g) > Cu (9.59E-01 μg/g) > Ni (5.34E-01 μg/g) > Cr (4.72E-02 μg/g) > Co (2.34E-02 μg/g) > As (1.77E-02 μg/g) > Cd (9.60E-03 μg/g) > Pb (5.00E-03 μg/g). Hierarchical cluster analysis has clustered 17 sampling locations into three clusters, whereby cluster 1 (S3, S4, S6, S15) located in residential areas and near to roads exposed to vehicle emissions, cluster 2 (S10, S12, S9, S7) located near Rawang town and cluster 3 (S13, S16, S1, S2, S8, S14, S11, S17, S5) located near industrial, residential and plantation areas. Emissions from vehicles, plantations and industrial activities were found as the main sources of heavy metals in classroom dust in Rawang. There is no association found between bioavailable heavy metal concentrations and respiratory symptoms, except for Cu (OR = 0.03). Health risks (non-carcinogenic and carcinogenic risks) indicated that there are no potential non-carcinogenic and carcinogenic risks of heavy metals in classroom dust toward children health.
Matched MeSH terms: Air Pollutants/analysis; Air Pollutants/pharmacokinetics; Air Pollution, Indoor/analysis*
The aim of this research is to explore the effect of biomass energy consumption on CO2 emissions in 80 developed and developing countries. To achieve robustness, the system generalised method of moment was used and several control variables were incorporated into the model including real GDP, fossil fuel consumption, hydroelectricity production, urbanisation, population, foreign direct investment, financial development, institutional quality and the Kyoto protocol. Relying on the classification of the World Bank, the countries were categorised to developed and developing countries. We also used a dynamic common correlated effects estimator. The results consistently show that biomass energy as well as fossil fuel consumption generate more CO2 emissions. A closer look at the results show that a 100% increase in biomass consumption (tonnes per capita) will increase CO2 emissions (metric tons per capita) within the range of 2 to 47%. An increase of biomass energy intensity (biomass consumption in tonnes divided by real gross domestic product) of 100% will increase CO2 emissions (metric tons per capita) within the range of 4 to 47%. An increase of fossil fuel consumption (tonnes of oil equivalent per capita) by 100% will increase CO2 emissions (metric tons per capita) within the range of 35 to 55%. The results further show that real GDP urbanisation and population increase CO2 emissions. However, hydroelectricity and institutional quality decrease CO2 emissions. It is further observed that financial development, foreign direct investment and openness decrease CO2 emissions in the developed countries, but the opposite results are found for the developing nations. The results also show that the Kyoto Protocol reduces emission and that Environmental Kuznets Curve exists. Among the policy implications of the foregoing results is the necessity of substituting fossil fuels with other types of renewable energy (such as hydropower) rather than biomass energy for reduction of emission to be achieved.
This study aims to determine the concentrations of surfactants in the surface microlayer (SML), subsurface water (SSW) and fine mode aerosol (diameter size
The focus of this work is to investigate the emission characteristics of a stationary diesel engine while utilizing an emulsion fuel from a novel preparation process. The emulsion preparation was performed in real time without using any surfactant. Instead of mechanically breaking the water down into droplets, the water is delivered thermally, by changing its phase from gas to liquid. Steam is used in this proposed process, where it will be converted into suspended water droplets once it meets colder diesel. The product is called steam-generated water-in-diesel emulsion fuel (S/D). The method is expected to reduce the moving components of a previous surfactant-less system; therefore, reducing costs and increasing the system reliability. The emission characteristics of S/D were compared with EURO 2 diesel (D2), and a conventional emulsion denoted as E10. E10 was prepared using 10% water (volumetric) and SPAN80 as a surfactant. The emission characterizations were carried out based on the exhaust gas of a single cylinder naturally aspirated CI engine fueled with D2, S/D, and E10. Compared to D2, both emulsions significantly reduced the emissions of nitrogen oxides (NOx) (E10 max ↓58.0%, S/D max ↓40.0%) and particulate matter (PM) (E10 max ↓20.0%, S/D max ↓57.0%).
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/prevention & control
We investigate the role of military expenditure on emission in USA during the period 1960-2015. To achieve the objectives of this study, two measures of military expenditure are utilised, while several timeseries models are constructed with the gross domestic product (GDP) per capita, population, energy consumption per capita, non-renewable energy consumption per capita, renewable energy consumption per capita, urbanisation, trade openness and financial development serving as additional determinants of air pollution. We also use ecological indicator as an alternative measure of pollution. Moreover, different timeseries methods are utilised including a likelihood-based approach with two structural breaks. The output of this research concluded that all the variables are cointegrated. It is found that military expenditure has mixed impact on CO2 emissions. Real GDP per capita, energy consumption per capita, non-renewable energy consumption per capita, population and urbanisation increase CO2 emissions per capita in the long-run, while renewable energy consumption, financial development and trade openness reduce it. There is also evidence for the mixed role of military expenditure, when ecological footprint is utilised as the environmental degradation index. From the output of this research, few policy recommendations are offered for the examined country.
Matched MeSH terms: Air Pollutants/analysis; Air Pollution/analysis*
Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.
Matched MeSH terms: Air Pollutants/analysis; Air Pollutants/economics; Air Pollution/analysis; Air Pollution/economics*
Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.
The main aim of this article is to examine empirically the impact of urbanization on carbon dioxide emissions in Singapore from 1970 to 2015. The autoregressive distributed lags (ARDL) approach is applied within the analysis. The main finding reveals a negative and significant impact of urbanization on carbon emissions in Singapore, which means that urban development in Singapore is not a barrier to the improvement of environmental quality. Thus, urbanization enhances environmental quality by reducing carbon emissions in the sample country. The result also highlighted that economic growth has a positive and significant impact on carbon emissions, which suggests that economic growth reduces environmental quality through its direct effect of increasing carbon emissions in the country. Despite the high level of urbanization in Singapore, which shows that 100 % of the populace is living in the urban center, it does not lead to more environmental degradation. Hence, urbanization will not be considered an obstacle when initiating policies that will be used to reduce environmental degradation in the country. Policy makers should consider the country's level of economic growth instead of urbanization when formulating policies to reduce environmental degradation, due to its direct impact on increasing carbon dioxide emissions.