Influence of transboundary air pollution into the atmosphere of Kathmandu Valley

In a rapidly urbanizing world, heavy air pollution and increasing surface temperature pose significant threats to human health and lives, especially in densely populated cities. In this study, we took an information theory perspective to investigate the causal relationship between diel land surface temperature (LST) and transboundary air pollution (TAP) from 2003 to 2020 in the Bangkok Metropolitan Region (BMR), which includes Bangkok Metropolis and its five adjacent provinces. We found an overall increasing trend of LST over the study region, with the mean daytime LST rising faster than nighttime LST. Evident seasonal variations showed high aerosol optical depth (AOD) loadings during the dry period and low loadings at the beginning of the rainy season. Our study revealed that TAP affected diel surface temperature in Bangkok Metropolis significantly. Causality tests show that air pollutants of two adjacent provinces west of Bangkok, i.e., Nakhon Pathom and Samut Sakhon, have a greater influence on the LST of Bangkok than other provinces. Also, the bidirectional relationship indicates that air pollution has a greater impact on daytime LST than nighttime LST. While LST has an insignificant influence on AOD during the daytime, it influences AOD significantly at night. Our study offers a new approach to understanding the causal impact of TAP and can help policymakers to identify the most relevant locations that cause pollution, leading to appropriate planning and management.

Trace metals in aerosols were observed at an urban site (Kumamoto, Kyushu, Japan) and a rural site (Cape Hedo, Okinawa, Japan) to investigate the relative contributions of transboundary air pollutants from mainland Asia and local air pollutants in western Japan. We used a cascade impactor to collect aerosols in five size classes. We apportioned the sources of the air masses on the basis of elemental components. Transboundary and local air pollutants were distinguished by use of the Pb/Cu and V/As ratios in selected size fractions of aerosols. The contribution of Pb (primarily from coal combustion in China) to total anthropogenic elements was greatest in spring, autumn, and winter in the 0.5–1 µm size fraction at both collection sites. The atmospheric environment at both sites was affected by this transboundary air pollutant. The contribution of Cu (primarily from local vehicle traffic) to total anthropogenic elements was greatest in all seasons in the 2.5–10 µm fraction at Kumamoto. Local air pollutants such as road dust, automobile brake abrasion, and waste incineration affected ambient air quality in Kumamoto. Because these pollutants resided mainly in the coarse aerosol fraction (> 2.5 µm), most of them were not transported to Cape Hedo in air bodies that we were able to trace to Kumamoto by backward projection. Based on our data the ambient air quality at Cape Hedo was little affected by local air pollutants emitted in the Kumamoto area.

Regional Environmental Cooperation on Transboundary Air Pollution in the Middle East and North Africa Inkyoung Kim (bio) Introduction1 Since the United Nations Conference on the Human Environment in 1972, international communities have endeavored to clarify the right and responsibility of states regarding transboundary pollution. Principle 21 of the 1972 Declaration at this Conference stated that countries have "the responsibility to ensure that activities within their jurisdiction or control do not cause damage to the environment of other States or of areas beyond the limits of national jurisdiction."2 Europe has been successful in tackling transboundary air pollution through the Convention on Long-range Transboundary Air Pollution in 1979. Furthermore, the Agenda 21 of the 1992 Declaration of the United Nations Conference on Environment and Development urged European countries to share their successful experiences with other regional communities to help them solve transboundary pollution issues. Have European experiences on transboundary air pollution been shared [End Page 1] with the Middle East and North Africa (MENA) region? What are the main sources of transboundary air pollution in MENA? To address those issues, what kinds of cooperative mechanisms have been created in this region? While many studies have attempted to tease out the motivations, success, and limitations of European regional environmental cooperation, regional environmental cooperation in MENA has been understudied. This study aims to investigate regional environmental cooperation in MENA, focusing on transboundary air pollution. Transboundary Pollution Most pollution problems are caused by local or regional sources, but pollution does not stay within and stop at national borders. It can travel by air or water thousands of miles. Transboundary flows of pollutants occur among countries in the same region as well as between continents. The United Nations (UN) defines transboundary pollution as "pollution that originates in one country but, by crossing the border through pathways of water or air, is able to cause damage to the environment in another country."3 More specifically, the United Nations Economic Commission for Europe (UNECE) defines transboundary air pollution as "air pollution whose physical origin is situated wholly or in part within the area under the national jurisdiction of one State and which as adverse effects in the area under the jurisdiction of another State at such a distance that it is not generally possible to distinguish the contribution of individual emission sources or groups of sources" in Article 1 of the 1979 Convention on Long-range Transboundary Air Pollution.4 It is known that some air pollutants circulate even globally and deposit on land and water bodies far from their original sources.5 Acid rain problems in Europe have led 51 countries to adopt the Convention on the Long-range Transboundary Air Pollution and various numbers of countries to ratify eight protocols on the reduction of sulfur, nitrogen oxides, volatile organic compounds, persistent organic pollutants, and ozone emissions since 1979.6 East Asia has also addressed acid rain [End Page 2] and dust sandstorms since the 1990s. As the Sahara Desert in North Africa and the whole region of the Middle East are the two hot spots of primary dust storms around the world, it is important to understand what the main sources of dust storms are and if MENA has developed any significant cooperative mechanisms to tackle transboundary impact of dust storms originated in this region. MENA Different entities identify the MENA region differently. For example, the World Bank identifies MENA as one of six regions of the world.7 It classifies the 19 countries or territories as part of the Middle East and North Africa, including Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, West Bank and Gaza, and Yemen.8 The OECD does not include Iran but does include Mauritania and Palestinian Authority instead of West Bank and Gaza. The UN does not identify MENA as one of its five regional groups.9 Its agencies and programs define the MENA region differently and sometimes contradictorily. For instance, United Nations Environment Programme has six regional offices including Africa, Asia Pacific, Europe, Latin American and the Caribbean, North America, and West Asia. The regional office for Africa covers the entire African continent...

Transboundary air pollution is one of the main sources of air pollution in island cities. However, the transboundary pollution confounded by local emission, meteorological conditions, and it is difficult to predict. Currently, most of urban air pollution prediction methods do not predict with transboundary air pollution. Therefore, we introduce a dynamic transboundary air pollution prediction approach based on convolutional recurrent neural networks(D-CRNN) which: (i) Divide the prediction inputs into prediction locations and transboundary air pollution sources; (ii) Use two different convolutional recurrent neural networks to solve the spatial-temporal feature of each inputs. (iii) Through a transboundary prediction network to integrate the spatial-temporal feature of prediction locations with the spatial-temporal feature of transboundary air pollution sources in a dynamic asynchronous method. Then use those mixed features to predict the air pollution. We evaluate our DCRNN model with the local atmospheric monitoring data in Kyushu, Japan and the transboundary air pollution data from 33 coastal cities in eastern Asia from January 2015 to July 2017. The results show that our D-CRNN model have achieved 86.2%, 78.6% accuracy of total prediction and transboundary air pollution in next 6 hours.

Air pollution in Japan has decreased from the elevated levels found during the period of high economic growth. However, in recent years there is increasing concern regarding air pollution caused by fine particulate matter (PM2.5) and its effect on human health. First, we investigated the seasonal and regional characteristics of air pollution caused by PM2.5 using ground measurements in China and Japan. Next, the cause of air pollution was classified as urban air pollution or transboundary air pollution. Air pollution in Fukuoka (in southwestern Japan) is strongly affected by transboundary air pollution. Nearly half of all air pollution in Tokyo (in eastern Japan) may be urban pollution caused by local emissions. PM2.5 high concentration from winter to spring is often caused by transboundary air pollution. On the other hand, PM2.5 high concentration from summer to autumn is greatly influenced by urban pollution. Air pollution caused by PM2.5 high concentration tends to decrease in Japan. This is due to both a reduction in PM2.5 emissions in Japan and a decrease in PM2.5 concentrations in China.

Contribution of local emissions and transboundary air pollution to air quality in Hong Kong during El Niño-Southern Oscillation and heatwaves

Trans-boundary air pollution in a city under various atmospheric conditions

Transboundary air pollution (TAP) and local air pollution (LAP) influence the air quality of urban areas. Fukuoka, located on the west side of Japan and affected by TAP from the Asian continent, is a unique example for understanding the contribution of LAP and TAP. Gaseous species and particulate matter (PM) were measured for approximately three weeks in Fukuoka in the winter of 2018. We classified two distinctive periods, LAP and TAP, based on wind speed. The classification was supported by variations in the concentration of gaseous species and by backward trajectories. Most air pollutants, including NOx and PM, were high in the LAP period and low in the TAP period. However, ozone was the exception. Therefore, our findings suggest that reducing local emissions is necessary. Ozone was higher in the TAP period, and the variation in ozone concentration was relatively small, indicating that ozone was produced outside of the city and transported to Fukuoka. Thus, air pollutants must also be reduced at a regional scale, including in China.

Air pollution continues to be an increasing problem in the largest metropolitan areas and regional industrial and commercial corridors in the world. This is also the case in Mexico. Current air quality trends in Mexico indicate that major urban centers continue to exceed the Mexican Ambient Air Quality Standards (MAAQS) for ozone (O3) and particulate matter with less than 10 microns of aerodynamic diameter (PM10), while other cities are starting to show warning signs of future air quality problems (Zuk et al., 2007). PM2.5 monitors are just starting to be deployed around the country, thus no extensive historical data is available on this pollutant. Some of the urban centers of concern share a common airshed with twin cities across the international border with the United States of America (USA), bringing additional complexity to the study of air pollution dynamics in the region. In this sense, transboundary air pollution across USA and Mexico has become a rising problem due to increased commercial and industrial activities in the border region. Trans-boundary air pollution has been studied at different levels in different areas of the border region (Mukerjee, 2001). Two main areas can be identified as the ones that have drawn most of the attention. The first one is the Lower California Area: Tijuana/San Diego, Mexicali/CalexicoImperial Valley (Figure 1). Here, most of the attention has been on primary PM (e.g., Osornio-Vargas et al., 1991; Chow et al., 2000; Sheya et al., 2000; Kelly et al., 2010), with some studies addressing secondary pollutants (e.g., Zielinska et al., 2001). The second area is the airshed formed by Ciudad Juarez-El Paso-Sunland Park. Perhaps, this area is the one that has received most of the attention regarding trans-boundary air pollution and in a more comprehensive fashion (Currey et al., 2005). Two of the key steps to improving air quality in a region are identifying, quantitatively, the emissions from sources that affect the area, and assessing how those emissions evolve in the atmosphere to impact pollutant concentrations. Both are difficult, and both can be subject to uncertainties. Air quality modeling is key to both steps because it provides a means to do

Effective environmental policies have to be based on robust information taking into consideration the existing trends. The objective of this paper is to facilitate data-driven decisions. Specifically we aim at policies that are built on officially reported emissions data making use of intuitive tools and interfaces to guide European air quality strategies with completeness and consistency. Transboundary (or cross-border) air pollution is a two-sided problem involving a polluter and a pollutee. The visualizations we have created allow a user to conceive the transboundary air pollution scheme from either the polluters’ or pollutees’ perspective. Based on the European Monitoring and Evaluation Program (EMEP) source-receptor matrices from 2003 to 2014, we develop comprehensive pollution monitoring systems. Our systems are created in visualization software in order to bring out the status, attributes and dynamics of transboundary air pollution. Our monitoring applications consist of different visualization modules. All of these modules carry their own information, which can be used separately or together to serve specific visualization tasks: either the polluters’ responsibility or the pollutees’ vulnerability. Several interactive interventions are integrated into each module to achieve particular visualization goals. Controls are added for the number of polluters, year of study, pollution level and geographical region within the extended EMEP area.

Air quality has gradually improved in many parts of China; however, air pollution is become more severe in the Fenwei Plain. Using OMI/Aura OMAERUV L2 and PM2.5 data, spatial autocorrelation analysis and back trajectory modeling were used to explore the spatio-temporal patterns of absorptive aerosols over the Fenwei Plain, and the dominant types, transmission paths, and potential source areas were identified. The main results can be summarized as follows:① Annual mean absorbing aerosol index (AAI) values increased between 2005 and 2019, with high period occurring in 2006, 2013, and 2017, with values exceeding 0.63. Xi'an and Linfen were identified as a 'high-high' cluster, with AAI showing poor spatial stability and a 15.3% increase in area over the past 15 years. In contrast, the area connecting Xi'an and Linfen, which occupies 24.2% of the total area of the region, was identified as a 'low-low' cluster, with a sharp drop of 6.2% in area; ② The Fenwei Plain has high AAI values across a large area in winter, exceeding 0.8 in Linfen and Xi'an, and 91.5% of the study area exceeding 0.6. Values were lower in spring (AAI>0.4) and autumn (AAI>0.3), with the lowest values occurring in summer. The atmospheric diffusion conditions in spring, autumn, and winter are poor, associated with anticyclonic high-pressure events. The observed high AAI values were significantly affected by atmospheric diffusion conditions, temperature, and precipitation; ③ Back trajectory and source contribution modeling showed that long-range transport of air masses from Xi'an and Linfen occurs from the northwest, and short-range transport air masses occurs from the east and south. Two long-range sand and dust source areas were determined (with northwestern and northern wind sources); two carbon source areas were identified (with eastern and southern wind sources); and one combined sand and carbon source area was identified (from the Loess Plateau). Of these sources, the northwestern wind source, the Loess Plateau, and the southern wind source have significant influence in Xi'an, and the eastern wind source and the Loess Plateau have a significant impact on Linfen. Linfen is little affected by the northwestern wind source and the dust from the northern wind source. Based on the spatial distribution of CO and its correlation with AAI, it is concluded that cardon in the dominant absorbent aerosol in Linfen dust and carbon are most important in Xi'an.

Since the Cold War detente, transboundary air pollution (TAP) has been on the pan-European diplomatic agenda. Under the Convention of Long-Range Transboundary Air Pollution (LRTAP) – the diplomatic framework for international cooperation on transboundary air pollution – eight protocols have been adopted through a science-driven and evolutionary negotiation process. The latest protocol to the LRTAP, ‘The 1999 Protocol to Abate Acidification, Eutrophication and Ground-Level Ozone’, signed in 1999 in Gothenburg, Sweden, sets out new regulations – national emission ceilings (NECs) and technologybased regulations – on sulphur oxides (SO2), nitrogen oxides (NOx), volatile organic compounds (VOCs) and ammonia (NH3), with ‘flexibility’ clauses.

Linfen in China’s Shanxi Province suffers severe air pollution in winter. Understanding the characteristics of air pollution and providing scientific support to mitigate such pollution are urgent matters. This study investigated the variations of PM2.5, PM10, NO2, SO2, O3, and CO in Linfen between December 1, 2019 and February 29, 2020. The mean concentrations of PM2.5, PM10, NO2, SO2, MDA8 (the maximum daily 8-h average) O3, and CO were 106.2, 139.4, 47.2, 41.0, 57.0 μg m−3, and 1.8 mg m−3, respectively. Large amounts of pollutants emitted by coal burning, industry, vehicles, and residents contributed to air pollution. Unfavorable meteorological conditions, such as lower temperature, weaker wind, higher relative humidity, and reduced planetary boundary layer height, made the situation worse. Fireworks and firecrackers set off to celebrate traditional Chinese festivals caused the concentration of PM pollutants to spike, with the maximum daily mean concentration of PM2.5 reached 314 μg m−3 and the peak hourly value reached 378.0 μg m−3. Suspensions of commercial and social activities due to COVID-19 reduced anthropogenic emissions, mainly from industry and transportation, which decreased the level of air pollutants other than O3. Analyses involving backward trajectory cluster, the potential source contribution function, and concentration weighted trajectory demonstrated that PM2.5 pollution mainly came from local emissions in Shanxi Province and regional transport from Inner Mongolia, Shaanxi, Hebei, Henan, and Gansu provinces. Shanxi and its surrounding provinces should adopt measures such as tightening environmental management standards, promoting the use of renewable energy, and adjusting the transportation structure to reduce regional emissions. This study will help policy-makers draft plans and policies to reduce air pollution in Linfen.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10661-021-09036-8.

Forest and land fires in Riau province of Sumatera increase along with the rapid deforestation, land clearing, and are induced by dry climate. Forest and land fires, which occur routinely every year, cause trans-boundary air pollution up to Singapore. Economic losses were felt by Indonesia and Singapore as the affected country thus creates tensions among neighboring countries. A high concentration of aerosols are emitted from fire which degrade the local air quality and reduce visibility. This study aimed to analyze the impact of the June 2013 smoke haze event on the environment and air quality both in Riau and Singapore as well as to characterize the aerosol properties in Singapore during the fire period. Air quality parameters combine with aerosols from Aerosol Robotic Network (AERONET) data and some environmental parameters, i.e. rainfall, visibility, and hotspot numbers are investigated. There are significant relationships between aerosol and environmental parameters both in Riau and Singapore. From Hysplit modeling and a day lag correlation, smoke haze in Singapore is traced back to fire locations in Riau province after propagated one day. Aerosol characterization through aerosol optical depth (AOD), Ångstrom parameter and particle size distribution indicate the presence of fine aerosols in a great number in Singapore, which is characteristic of biomass burning aerosols. Fire and smoke haze even impaired economic activity both in Riau and Singapore, thus leaving some accounted economic losses as reported by some agencies.

Southeast Asia has been experiencing severe air pollution due to its substantial local emissions and transboundary air pollution (TAP), causing significant health impacts. While literature focused on air pollution episodes in Southeast Asia, we have yet to fully understand the contributions of local emission sectors and TAP to air quality in the region annually. Herein we employed air quality modeling with the species tagging method to first assess the contributions of source sectors and locations to fine particulate matter (PM2.5) and ozone (O3) in Southeast Asia and to hence quantify the resultant health impacts. Our results show that air pollutant exposure was associated with ∼ 900 thousand premature mortalities in Southeast Asia every year. Of which, 77 % and 23 % were due to local emissions and TAP in the region, respectively. ∼ 87 % of the premature mortalities due to local emissions were induced by PM2.5 exposure, whereas the remaining were due to O3 exposure. PM2.5-related health impacts were dominated by industrial (45 %) and residential (17 %) emissions, and O3-related impacts were mainly due to biogenic (40 %) and road transport (24 %) emissions. Furthermore, the health impacts of TAP were particularly adverse in Brunei, East Timor, Singapore, Laos, and border regions.