High Emissions Of Air Pollutants Research Articles

State of gaseous air pollutants and resulting health effects in Karachi, Pakistan.

Karachi, Pakistan, is a priority site for air pollution research due to high emissions of air pollutants from vehicular traffic, industrial activities, and biomass burning, as well as rapid growth in population. The objectives of this study were to investigate the levels of gaseous pollutants (NO, NO2, O3, HNO3, and SO2) in Karachi, to determine temporal and seasonal variations, to compare Karachi's air quality with other urban centers, to identify relationships with meteorological conditions, to identify source characterization, and to perform a backward-in-time trajectory analysis and a health impact assessment. Daily samples of gaseous pollutants were collected for six consecutive weeks in each of the four seasons for a year. Daily maximum concentrations of NO (90 parts per billion by volume (ppbv)), NO2 (28.1 ppbv), O3 (57.8 ppbv), and SO2 (331 ppbv) were recorded in fall, while HNO3 (9129 parts per trillion by volume (pptv)) was recorded in spring. Seasonal average concentrations were high in winter for NO (9.47 ± 7.82 ppbv), NO2 (4.84 ± 3.35 ppbv), and O3 (8.92 ± 7.65 ppbv), while HNO3 (629 ± 1316 pptv) and SO2 (20.2 ± 39.4 ppbv) were high in spring and fall, respectively. The observed SO2 seasonal average concentration in fall (20.2 ± 39.4) was 5 times higher than that in summer (3.97 ± 2.77) with the fall 24-h average (120 ppbv) exceeding the WHO daily guideline (7.64 ppbv) by a factor of about 15.7. A health impact assessment estimated an increase of 1200 and 569 deaths due to short-term exposure to SO2 in fall and spring, respectively. Chronic daily intake estimated risk per 1000 was 0.99, 0.47, 0.45, and 0.26 for SO2 in fall, NO in winter, O3 in winter, and NO2 in spring, respectively. This study confirms the effect of poor urban air quality on public health and demonstrated the influence of photochemical reactions as well as unfavorable meteorological conditions on the formation of secondary pollutants.

Analysis of the air pollution reduction and climate change mitigation effects of the Three-Year Action Plan for Blue Skies on the “2+26” Cities in China

City clusters play an important role in air pollutant and greenhouse gas (GHG) emissions reduction in China, primarily due to their high fossil energy consumption levels. The "2+26" Cities, i.e., Beijing, Tianjin and 26 other perfectures in northern China, has experienced serious air pollution in recent years. We employ the Greenhouse Gas and Air Pollution Interactions and Synergies model adapted to the "2+26" Cities (GAINS-JJJ) to evaluate the impacts of structural adjustments in four major sectors, industry, energy, transport and land use, under the Three-Year Action Plan for Blue Skies (Three-Year Action Plan) on the emissions of both the major air pollutants and CO2 in the "2+26" Cities. The results indicate that the Three-Year Action Plan applied in the "2+26" Cities reduces the total emissions of primary fine particulate matter with an aerodynamic diameter of ≤ 2.5μm (PM2.5), SO2, NOx, NH3 and CO2 by 17%, 25%, 21%, 3% and 1%, respectively, from 2017 to 2020. The emission reduction potentials vary widely across the 28 prefectures, which may be attributed to the differences in energy structure, industrial composition, and policy enforcement rate. Among the four sectors, adjustment of industrial structure attains the highest co-benefits of CO2 reduction and air pollution control due to its high CO2 reduction potential, while structural adjustments in energy and transport attain much lower co-benefits, despite their relatively high air pollutant emissions reductions, primarily resulting from an increase in the coal-electric load and associated carbon emissions caused by electric reform policies..

Z-number-based AQI in rough set theoretic framework for interpretation of air quality for different thresholds of PM2.5 and PM10.

Kolkata has a reputation for being one of the world’s most polluted cities, particularly in the post-monsoon months of October, November, and December. Diwali, a Hindu festival, coincides with these months where a large number of firecrackers are set off followed by high emissions of air pollutants. As a result, the air quality index (AQI) deteriorates to “very poor” (301 ≤ AQI ≤ 400) and “poor” (201 ≤ AQI ≤ 300) categories. This situation stays for several days to a month. The present study aims to identify the thresholds for PM2.5 and PM10 that cause the AQI of Kolkata to deteriorate to “very poor” and “poor.” For this purpose, we have used a rough set theory-based condition-decision support system to predict the aforementioned categories of AQI. We have developed a Z-number-based novel quantification measure of semantic information of AQI to assess the reliability of the outcomes, as generated from the condition-decision-based decision rules, during post-monsoon season. The result reveals the best possible forecast of AQI with linguistic summarization of the reliability or confidence for different threshold ranges of PM10 and PM2.5. Inverse-decision rules based on rough set theory are utilized to justify and validate the forecasts. The explainability of the condition-decision support system is demonstrated/visualized using a flow graph that maps rough-rule-based different decision paths between input and output with strength, certainty, and coverage. The investigation resulted in an advanced intelligent environmental decision support system (IEDSS) for air-quality prediction.

Design of air quality monitoring network of Luanda, Angola: Urban air pollution assessment

The World Health Organization has been making recommendations on assessing and monitoring air quality for human health protection. Its implementation implies a spatial distribution plan for air quality monitoring sites, especially in populated cities where high emissions of air pollutants with anthropogenic origin are observed. Therefore, the present study aimed: (i) to propose the spatial distribution of air quality monitoring sites in the city of Luanda (capital of Angola); and (ii) to determine the PM2.5, PM10, NO2, SO2 and CO concentrations at two monitoring sites (Avenida Deolinda Rodrigues, ADR, and Condomínio Vale do Talatona, CVT), during four weeks for each site. Due to the absence of national legislation, air quality measurements were then compared to the limits defined by the European Union through Directive, 2008/50/EC. At ADR site, the average concentrations for PM10, PM2.5 and SO2 exceeded the limits recommended for human health. At CVT site, CO, NO2, PM10, PM2.5 and SO2 concentrations were lower than the values measured at ADR site, due to the relative location of important fixed air pollutant emission sources: airport, thermoelectric plants, refinery, cement plants and the Port of Luanda. The weekend effect was also assessed at both sites. At ADR site, NO2, SO2, PM2.5 and PM10 concentrations were higher at weekends than on weekdays. At CVT site, NO2, SO2 and PM2.5 concentrations were higher on weekdays compared to weekends. The present research will contribute to the decision-making process by the environmental health regulator in Luanda.

Systematic Evaluation of a Multi-Lane Green-Driving Algorithm in a Mixed Connected Environment

Vehicles traveling under oscillated traffic have low energy efficiency and high air pollutant emissions. Green driving with the help of connected vehicles (CVs) attracts a lot of research effort to improve vehicle energy efficiency. However, it is very challenging to perform green driving on multi-lane freeways under a mixed connected environment. In the researchers’ previous work, one innovative green-driving algorithm was proposed to solve the multi-lane problem with only one CV. In this study, a systematic analysis of the algorithm is conducted to understand its benefits and limitations on smoothing traffic oscillations. The effect of the steady states and the number of lanes is also analyzed. In addition, the algorithm is extended to a more general scenario with multiple CVs. The extended system coordinates multiple CVs to form multiple moving bottlenecks to mitigate traffic oscillation more efficiently as well as providing more realistic instructions to CVs. The evaluation of the extended system concludes with the most effective strategies to control CVs to smooth oscillations.

Spatial Heterogeneity Influences of Environmental Control and Informal Regulation on Air Pollutant Emissions in China.

High air pollutant emissions in China have become serious environmental issues threatening public health. While spatial heterogeneity plays an important role in environmental regulation in China, it is necessary to analyze the spatial heterogeneity influences of air pollution control policies and informal environmental regulation on air pollutant emissions in China. Based on the quantification of air pollution control policies, this paper incorporates the central government’s policy formulation and local government’s policy implementation into the intensity of air pollution control policy. This paper uses the panel data of China’s 30 provinces to examine the spatial impact of air pollution control policy and informal environmental regulation on air pollutant emissions. The results show that (a) air pollutant emissions represented by soot and dust emission intensity has a significant positive spatial spillover effect; (b) air pollution control policy and informal environmental regulation play significant inhibitory roles in air pollutant emissions; (c) informal environmental regulation has a negative moderating effect on the negative relationship between air pollution control policy and air pollutant emissions. Other implications for environmental management have also been discussed.

Estimating transboundary transported anthropogenic sulfate deposition in Japan using the sulfur isotopic ratio

High emissions of air pollutants from Northeast Asia are strongly influenced by air quality as well as by ecosystems. This study investigated the spatiotemporal variations in the sulfur isotopic ratio (δ34S) in atmospheric deposition at eleven monitoring stations in Japan from 2011 to 2016 and estimated the amount of transboundary transported anthropogenic sulfate (TRB) deposition using mass balance calculations. The δ34S of sulfate in precipitation ranged from -0.42 to +22.7‰. Sea salt (SS), TRB, and domestic anthropogenic sources (DOM) were the dominant sources of sulfate deposition in Japan. TRB sulfate deposition was largest on the Sea of Japan side, with an annual average value of 1.5 ± 0.3-6.9 ± 0.5 mg m-2 d-1 (36-44%), followed by Mt. Happo (4.5 ± 0.1 mg m-2 d-1; 88%), the Pacific Ocean side (1.5 ± 0.8, 4.3 ± 0.9 mg m-2 d-1; 24-50%), and the remote islands in the North Pacific Ocean (1.1 ± 0.2, 2.0 ± 0.8 mg m-2 d-1; 19-32%). TRB sulfate deposition on the Sea of Japan side was 2-12 times higher in winter and 1-2 times higher in summer than that of DOM. In contrast, TRB sulfate deposition on the Pacific Ocean side was 1.5-3 times higher in summer than in winter due to high precipitation levels. In Tokyo, the annual contribution from DOM sulfate deposition is approximately three times higher than that from TRB. Annual TRB sulfate deposition is lowest at Ogasawara at 1.1 ± 0.2 mg m-2 d-1, and the annual oceanic DMS contribution to sulfate deposition is high, accounting for 1.3 mg m-2 d-1 (20 ± 6%). The contribution of Asian dust was estimated to be 1-5.2 mg m-2 d-1(3-6%), which occurred in a single Asian dust event on the Sea of Japan side.

The impact of meteorological changes from 2013 to 2017 on PM2.5 mass reduction in key regions in China

In 2013, China issued the “Action Plan for the Prevention and Control of Air Pollution” (“Ten Statements of Atmosphere”) and implemented a series of pollution reduction measures from 2013 to 2017. In key regions of China, the mass concentrations of particulate matter with aerodynamic equivalent diameters less than 2.5 μm (PM2.5) have dropped significantly. However, the contributions of meteorological changes to PM2.5 reduction are largely uncertain, which has attracted particular concern from the government and the public. Here, we investigated the impact of large-scale and boundary layer (BL) meteorological conditions on aerosol pollution and estimated the contributions of meteorological changes to PM2.5 reduction based on in-depth analysis and diagnosis of various observed meteorological elements and an integrated pollution-linked meteorological index (PLAM, which is approximately and linearly related to PM mass concentration). In this study, we found that the meteorological conditions worsened in 2014 and 2015 and improved in 2016 and 2017 relative to those in 2013 in key regions in China. In 2017 relative to 2013, only ~5% (approximately 13% of the total PM2.5 decline) of the 39.6% reduction in PM2.5 mass concentrations can be attributed to meteorological changes in the Beijing-Tianjin-Hebei (BTH) region, and only ~7% (approximately 20% of the total PM2.5 decline) of the 34.3% reduction can be attributable to meteorological changes in the Yangtze River Delta (YRD) region. Overall, the PM2.5 reduction due to meteorological improvement is much lower than the observed PM2.5 reduction in these areas, which indicates that emission reduction during the five-year implementation of the “Ten Statements of Atmosphere” is the dominant factor in the improvement in air quality. The changes in meteorology and climate are conducive to PM2.5 reduction but do not dominate the substantial improvement in air quality. Similar to the above regions, in the Pearl River Delta (PRD) region, the impact of meteorological changes on the annual averaged PM2.5 concentration from 2013 to 2017 was relatively weak, and the PM2.5 reduction was mainly due to emission reductions. During winter 2017 (January, February, and December of this year), the meteorological conditions improved ~20% in the BTH region (observed total PM2.5 reduction: 40.2%) and ~30% in the YRD region (observed total PM2.5 reduction: 38.2%) relative to those in 2013, showing the meteorological factors played more important role in the decrease of PM2.5 in winter of these years in the two regions, respectively. The meteorological conditions in winter 2016 were 14% better than those in winter 2017, but the PM2.5 reduction in winter 2016 was still less than that in winter 2017, reinforcing the significant contributions of the increasing efforts to reduce PM2.5 emissions in 2017. The substantial progress of strict emission measures was also confirmed by a comparison of several persistent heavy aerosol pollution episodes (HPEs) with similar meteorological conditions. It is found that the decrease of PM2.5 mass caused by emission reduction increases year by year, especially the decrease of PM2.5 concentration in 2016 and 2017. In China, HPEs mainly occur in winter, when meteorological conditions are approximately 40--100% worse than in other seasons. This worsening is partly due to the harbor effect of high topography, including downdrafts and the weak wind zone, and partly due to the increasingly stable regional BL structure caused by climate warming. For the formation of HPEs, it occurred under regional stagnant and stable conditions associated with upper-level circulation patterns, including the zonal westerly winds type and high-pressure ridges. After pollution formation, PM2.5 with mass accumulated to a certain degree can further worsen the BL meteorological conditions. The feedback effect associated with worsening conditions dominates PM2.5 mass explosive growth. In the context of high air pollutant emissions in China, unfavorable meteorological conditions are the necessary external conditions for the formation and accumulation of HPEs. Therefore, reducing aerosol pollution significantly during the earlier transport stage is critical in reducing persistent HPEs. Currently, even under favorable meteorological conditions, allowing emissions without restriction is also not advisable because aerosol pollution allowed to accumulate to a certain extent will significantly worsen the BL meteorological conditions and close the “meteorological channels” available for pollution dispersion.

Spatial and seasonal characteristics of particulate matter and gaseous pollution in China: Implications for control policy

By employing the air pollution data including particular matter (PM) and gaseous pollutants (SO2, NO2, CO, and O3) measured over 130 cities in China from April 2014 to March 2015, the spatial and seasonal variations of air pollution are analyzed. The 9 representative regions including Beijing, Tianjin, and Hebei (BTH), Yangze River Delta (YRD), central China (CC), Sichuan Basin (SB), northeast China (NEC), northwest China (NWC), Pearl River Delta (PRD), Yungui Plateau (YP), and Tibet, are chose to quantify the conditions of PM and gaseous pollution. According to the magnitudes of PM2.5 from high to low, the regions are listed in sequence as BTH, CC, SB, YRD, NEC, NWC, PRD, YP, and Tibet. The spatial patters of gaseous pollutants except O3 are generally consistent with PM's. The CO maximum is found in BTH and NWC while the O3 maximum in YRD, PRD, and Tibet. The seasonal cycles of SO2 and NO2 are quite similar to that of PM, but the SO2 is overall higher than NO2 in winter over the northern China while the opposite is true over the southern China. The O3 concentrations are generally low in winter, but high in spring and summer due to active photochemical reaction when temperature is high. The percentage of haze days (daily PM2.5 exceeds NAAQS Grade II, i.e. 75 μg m−3) to the entire year is 45, 32 and 29%, respectively over BTH, CC, and SB, three most PM pollution regions during the study period. Although the most severe haze region occurs in BTH (139 days) from annual mean, the most severe winter in SB (54 days) owing to its basin landform and high air pollutant emissions. In contrary to PM pollution, gaseous pollution in China are overall quite trivial.

SSP3: AIM implementation of Shared Socioeconomic Pathways

Abstract This study quantifies the Shared Socioeconomic Pathways (SSPs) using AIM/CGE (Asia-Pacific Integrated Assessment/Computable General Equilibrium). SSP3 (regional rivalry) forms the main focus of the study, which is supposed to face high challenges both in mitigation and adaptation. The AIM model has been selected as the model to quantify the SSP3 marker scenario, a representative case illustrating a particular narrative. Multiple parameter assumptions in AIM/CGE were differentiated across the SSPs for quantification. We confirm that SSP3 quantitative scenarios outcomes are consistent with its narrative. Moreover, four key features of SSP3 are observed. First, as SSP3 was originally designed to contain a high level of challenges to mitigation, mitigation costs in SSP3 were relatively high. This results from the combination of high greenhouse gas emissions in the baseline (no climate mitigation policy) scenario and low mitigative capacity. Second, the climate forcing level in 2100 for the baseline scenarios of SSP3 was similar to that of SSP2, whereas CO 2 emissions in SSP3 are higher than those in SSP2. This is mainly due to high aerosol emissions in SSP3. A third feature was the high air pollutant emissions associated with weak implementation of air quality legislation and a high level of coal dependency. Fourth, forest area steadily decreases with a large expansion of cropland and pasture land. These characteristics indicate at least four potential uses for SSP3. First, SSP3 is useful for both IAM and impact, adaptation, vulnerability (IAV) analyses to present the worst-case scenario. Second, by comparing SSP2 and SSP3, IAV analyses can clarify the influences of socioeconomic elements under similar climatic conditions. Third, the high air pollutant emissions would be of interest to atmospheric chemistry climate modelers. Finally, in addition to climate change studies, many other environmental studies could benefit from the meaningful insights available from the large-scale land use change resulting in SSP3.

Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

Global warming has obtained more attention because of increasing global mean surface temperature since the late 19th century. Urbanization is considered an important contributor to global warming in big cities. Cairo is one of the heavily populated cities in the world, which has rapid urbanization that resulted in remarkable temperature variation compared to the rural surrounding areas. This phenomenon is known as Urban Heat (UHIs) that influence both local and regional climate, environment, and socio-economic development. In this research time series of satellite images were used to map the spatial variability of Land Surface Temperature (LST) and Heat Islands in Cairo city. Historical and contemporary record of Landsat images nearly 16 dates from 1990 to 2014 were used to retrieve LST and land cover categories. Results showed that LST was highly influenced by Land use and land cover changes; heat island effect was dominant in urbanized areas, bare/desert land and industrial zones (e.g. Shobra El-khema). The industrial zones, in particular, were possibly high due to the aluminum roof material plus the thermal energy from production activities and high emissions of air pollutants. The pattern of spatial distribution of heat islands has significantly changed from bare/desert land and built up areas (as warmer) to cultivated land and water bodies (as colder). To analyze the relationship between Heat Islands and land-cover changes a quantitative correlation between LST and Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Build-up Index (NDBI) was employed. It was found that the correlation between LST and NDVI and MNDWI was negative with r 2 of 0.8 and 0.57 respectively; however, the correlation with NDBI was strongly positive with r 2 of 0.81. It is anticipated that the outcomes of this research will contribute to GEOSS for global integration and correlation.

Modeling study of the 2010 regional haze event in the North China Plain

Abstract. The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was applied to simulate a haze event that happened in January 2010 in the North China Plain (NCP), and was validated against various types of measurements. The evaluations indicate that WRF-Chem provides reliable simulations for the 2010 haze event in the NCP. This haze event was mainly caused by high emissions of air pollutants in the NCP and stable weather conditions in winter. Secondary inorganic aerosols also played an important role and cloud chemistry had important contributions. Air pollutants outside Beijing contributed about 64.5 % to the PM2.5 levels in Beijing during this haze event, and most of them are from south Hebei, Tianjin city, Shandong and Henan provinces. In addition, aerosol feedback has important impacts on surface temperature, relative humidity (RH) and wind speeds, and these meteorological variables affect aerosol distribution and formation in turn. In Shijiazhuang, Planetary Boundary Layer (PBL) decreased about 278.2 m and PM2.5 increased more than 20 µg m−3 due to aerosol feedback. It was also shown that black carbon (BC) absorption has significant impacts on meteorology and air quality changes, indicating more attention should be paid to BC from both air pollution control and climate change perspectives.

Air Pollution Monitoring and Control System for Subway Stations Using Environmental Sensors

The metropolitan city of Seoul uses more energy than any other area in South Korea due to its high population density. It also has high emissions of air pollutants. Since an individual usually spends most of his/her working hours indoors, the ambient air quality refers to indoor air quality. In particular, PM10concentration in the underground areas should be monitored to preserve the health of commuters in the subway system. Seoul Metro and Seoul Metropolitan Rapid Transit Corporation measure several air pollutants regularly. In this study, the accuracy of an instrument for PM measurement using the light scattering method was improved with the help of a linear regression analysis technique to continuously measure thePM10concentrations in subway stations. In addition, an air quality monitoring system based on environmental sensors was implemented to display and record the data of PM10, CO2, temperature, and humidity. Through experimental studies, we found that ventilation fans could improve air quality and decrease PM10concentrations in the tunnels effectively by increasing the air flow rate.