TESTING THE QUALITY OF THE ENVIRONMENT THROUGH THE LICHENOINDICATION METHOD

Air pollution is a global problem in urban and rural areas, mainly related to vehicular traffic and industrial activities. Bromeliads have been widely used globally for active and passive air quality monitoring. However, a systematic review to facilitate the access and analysis of this information is yet to be made available. The objective of this work was to systematically review the use of bromeliads in biomonitoring of the air quality from articles published between 1990 and 2023 to analyze the progress and impact of the research related to the type of monitoring, species used, pollutants measured, and sampling protocols. The search was carried out in global (Scopus, ISI Web of Science, ScienceDirect, and MDPI) and regional (SciELO and Redalyc) databases with a total of 60 scientific articles, where the neotropical region with 31 articles and the Nearctic region with 11 articles were the most influential regions. In addition, more than 90% of the research has been published in high‐impact journals (quartile 1). Passive monitoring registered 25 articles compared to active monitoring with 23, with 23 species of the family Bromeliaceae, predominantly the genus Tillandsia. Tillandsia usneoides was the most common species used in active monitoring, while Tillandsia recurvata was used for passive monitoring. The measurement of heavy metals was the preferred technique (93% of the studies) for air quality monitoring, where Zn, Fe, Pb, Cr, and Mn and the nonessential elements K, Ca, and Na were the most measured. Results obtained by different research groups cannot be compared directly because different methodologies have been used, highlighting the importance of standardized techniques for future work. Thus, as a contribution in this direction, we propose a protocol to facilitate or standardize the selection of the proper methodology for developing air quality monitoring using bromeliads.

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...

Brain Fog: Does Air Pollution Make Us Less Productive?

This paper examines the question whether the scientific knowledge framework produced in the context of the Convention on Long-range Transboundary Air Pollution (LRTAP) can keep its credibility, legitimacy and relevance when used in a different policy arena, e.g. the European Commission (EC) of the European Union. The paper combines a conceptual framework for considering effective assessments with the notion of boundary work and co-production of science and policy to examine differences between the roles and division of tasks between scientists and policy makers in the two different policy contexts. The paper concludes that, despite the differences between the two policy settings, user characteristics and the historical context are to a certain extent similar in LRTAP and the EU Clean Air for Europe Programme (CAFE), and that participants in the two processes partially overlap and tackle the same policy problem. The scientific knowledge framework as developed within LRTAP can maintain credibility, legitimacy and relevance when it is used in CAFE if certain conditions are fulfilled. One condition is the effective functioning of LRTAP, because the CAFE assessment process remains also dependent on the LRTAP process. Data collection and mapping efforts in the context of LRTAP form also the basis for the analyses within CAFE. Furthermore, a broadly embedded scientific basis is needed in the countries to enable each country to follow or relate to the analyses commissioned by the EU. The conceptual framework and concept of boundary work used in this paper turned out to be helpful in focusing on the dynamic relationship between science and policy.

Air pollution is a global cause of concern due to the severe environmental impacts such as acid rain, smog, damage to crops and infrastructure; and wide-ranging health impacts like eye irritation, nasal irritation or chronic respiratory illness, depending on factors like type of pollutant, its concentration and exposure duration. It is leading contributor to the global burden of diseases and deaths. According to the World Health Organization (WHO), globally 4.2 million people die due to ambient (outdoor) air pollution while 3.8 million die of indoor air pollution (from cookstoves and domestic fuels). Results from our air quality monitoring stations put Rwanda among the country having air pollution levels that exceed WHO air quality especially on PM2.5 and PM10. This high level of air pollution concentration contributed to more than 5500 deaths in 2017 in Rwanda. Thus, assessing the air quality is of extreme importance to develop policy interventions and pollution control measures to safeguard the environmental and human wellbeing in a particular area. To address this, in 2019 Rwanda started it first ever air quality monitoring system with 22 Real-time, Affordable, Multi-Pollutant (RAMP) air quality monitors and one reference station to provide a complete picture of air quality in the Rwanda. Stations are installed country-wide focusing on the City of Kigali and Secondary cities in the country and measure criteria pollutants like Particulate matter, Sulphur Dioxide, Oxides of nitrogen, Carbon monoxide, and secondary pollutants like Ozone. The monitoring of pollutants is carried out for 24 hours every day and is continuous along the year to have annual trend of air pollution in Rwanda. The data obtained are also used to calculate the Air Quality Index (AQI), a comprehensive and easy to interpret value, representing the air quality status and disseminated through a web-based portal(https://aq.rema.gov.rw/). To improve air quality management in Rwanda, the government of Rwanda and the government of Finland has started a partnership through FINKERAT Project. In its different capacity building programs FINKERAT has helped in integrating satellite data into Rwanda's air quality monitoring framework to address limitations in spatial coverage. This integration not only expands the geographical reach of monitoring but also enhances the granularity of information available. Satellite data, with its broad coverage and high temporal resolution, will complements ground-based measurements by providing a holistic view of air quality dynamics, especially in remote or challenging to reach areas. Through this FINKERAT project Rwanda’s air quality monitoring is building the capacity in air quality modelling that will lead to air quality forecasting. Forecasting air pollution will help in providing early warnings to communities and authorities to minimize health risks, it will also optimize resource allocation, supports evidence-based policies, and helps businesses adapt operations, fostering resilience and sustainability in the face of pollution challenges.

November 2019 marked the 40th anniversary of the adoption in Geneva of the Convention on Long-Range Transboundary Air Pollution, which aims primarily to reduce the damage to human health and the environment caused by air pollution. Over the years, the Convention has been extended by eight protocols that identify measures to control emissions of basic air pollutants. The effortsundertaken under the Convention have been instrumental in bolstering international cooperation to limit the pollution with sulphur and nitrogen oxides as well as to reduce emissions of other pollutants. The European Monitoring and Evaluation Programme (EMEP) has been developed complete with modelling and forecasting air pollution levels and pollutant fluxes. Robust information has been gathered in the EMEP databases. However, much remains to be done, and air pollution is still a challenge in theUN ECE region.

Air pollution has become an integral part of modern life. The main source of air pollution can be considered combustion processes associated with energy-intensive corporate activities. Energy companies consume about one-third of the fuel produced and are a significant source of air pollution [1]. State and public air quality monitoring networks were created to monitor the situation. Public monitoring networks are cheaper and have more coverage than government ones. Although the state monitoring system shows more accurate data, an inexpensive network is sufficient to inform the public about the presence or absence of pollution (air quality). In order to inform the public, the idea arose to test the possibility of detecting types of pollution using data from cheap air quality monitoring sensors. In general, to use a cheap sensor for measurements, it must first be calibrated (corrected) by comparing its readings with a reference device. Various mathematical methods can be used for this. One of such method is neural network training, which has proven itself well for correcting PM particle readings due to relative humidity impact [2].The idea of using a neural network to improve data quality is not new, but it is quite promising, as the authors showed in [3]. The main problem to implement this method is connected with a reliable dataset for training the network. For this, it is necessary to register sensor readings for relatively clean air and for artificially generated or known sources of pollution. Training the neural network on the basis of collected data can be used to determine (classify) types of air: with pollution (pollutant) or without. For this, an experiment was set up in the "ReLab" co-working space at the Taras Shevchenko National University of Kyiv. The sensors were placed in a closed box, in which airflow ventilation is provided. The ZPHS01B [4] sensor module was used for inbox measurements, as well as, calibrated sensors PMS7003 [5] and BME280 [6]. Additionally, IPS 7100 [7] and SPS30 [8] were added to enrich the database for ML training. A platform based on HiLink 7688 was used for data collecting, processing, and transmission.Data was measured every two seconds, independently from each sensor. Before each experiment, the room was ventilated to avoid influence on the next series of experiments.

PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization

<div> <p>It is well established that the high level of particulate matter is a leading cause of premature mortality and disease worldwide and especially in South Asia (Global Burden of Disease Study, 2019). The ground-based air quality (AQ) monitoring stations are used to calculate economic loss, premature mortality and validate the conversed PM2.5 concentration from satellite-based Aerosol Optical Depth (AOD) data. Over India, 793 manual monitoring air quality (AQ) monitoring stations and 307 automated AQ monitoring station are presently operating under the aegis of National Air Quality Monitoring Programme and Central Pollution Control Board respectively. However, studies addressing the spatial representativeness of the data generated from the AQ monitoring stations over India are very limited and therefore, it is unclear that whether the existing stations are sufficient to reflect the average ambient AQ over different Indian cities. </p> </div><div> <p>The present study intends to classify the existing AQ monitoring stations on the basis of spatial representativeness and derive a general conceptual framework for commissioning representative AQ monitoring sites for Indian cities. The methodology involves analysis of land use, populations and air quality data for the existing air quality stations in million plus Indian cities. A case study was conducted for Pune (18.5° N, 73.8° E), a western Indian metro city with 3.15 million population (Census, 2011). Using the night-time light data and high resolution PM2.5, population exposure hotspots over Pune city were identified. It was observed that not only at the midst of the municipal area, population exposure hotspots can be identified at the peripheral region of PMC/PNMC which certainly signify the role of rapid developmental activity and urban agglomeration over Pune city. The existing air quality monitoring sites are located majorly in the pollution hotspots in the city center region and therefore installing AQ monitoring stations (co-located  with weather station) at the rapidly developing parts of the city is highly recommended. The present land use pattern and the location of existing monitoring sites suggests lack of urban background monitoring stations which indicates the gap of knowledge in monitoring the average air quality responsible of long-term health effect over Pune. The prevalence of AQ monitoring stations in the road junction points and near to metro construction works might overestimate the exposure estimate of the general population in the city.   </p> </div>

The Mediterranean Basin is recognized as a Biodiversity Hotspot for conservation priorities, mainly due to the high biodiversity of plants. Different environmental protection categories of the territory can help to ensure the long-term preservation of biodiversity and habitats. Among them, National Park is the legal category providing the highest protection for nature areas with remarkable ecological and cultural value. These areas represent valuable locations for long-term monitoring of ecological conservation and ecosystem functioning. The Spanish National Parks Network comprises 16 natural areas, including mountain ranges, volcanic habitats, different types of forest, scrublands and grasslands, and aquatic and marine ecosystems. All the National Parks are part of the European Natura 2000 network. Moreover, 6 of them are part of the LTER-Spain network. By law, National Parks must include a long-term Monitoring and Evaluation Plan for the Network, including ecological, sociological, and functional monitoring programmes. Within the ecological monitoring programme, besides flora, fauna and ecosystem functioning assessments, climate is also considered as one of the main drivers of global change. Tree crown condition is assessed in all the parks with forests following the methodology of ICP-Forest Level I. The next step is to include air quality, since atmospheric pollution is another important driver of global change that can affect ecosystem health and functioning. Particularly tropospheric ozone and atmospheric nitrogen deposition are expected to be exceeding the limit values for vegetation protection. Air quality monitoring networks are mainly focused on the protection of human health, thus some very valuable areas for biodiversity might not be represented by air monitoring stations. In addition, atmospheric deposition is not included at the air quality monitoring stations. Moreover, the extension of some of the National Parks, the complex orography and the different climate, morphology and air pollution sources that influence the National Parks, represent a challenge for managers to design an air quality monitoring program with an efficient use of resources. To address this challenge and provide the information needed to design an air quality network in National Parks, a new project is being developed combining local monitoring with air quality modelling. The spatial representativeness of background rural monitoring stations has been assessed through relating CHIMERE chemistry-transport (v2013) model simulations at 5 x 5 km2 resolution and measured data at the stations in Spain (including the Canary Islands) for the years 2019-2023 considering NO2, SO2, O3, PM10 and atmospheric deposition. With this analysis, the suitability of current monitoring stations to represent the air quality in National Parks is being assessed. A risk analysis of the effects on terrestrial ecosystems using CLRTAP critical levels and loads is being performed using the CHIMERE model in the different National Parks. Additionally, a new air quality and deposition monitoring programme has been initiated in 2025 in all the National Parks to measure NO2, SO2, O3 and NH3 using passive samplers and atmospheric wet deposition using bulk collectors connected to ion-exchange resins. Local monitoring data will be used to determine the spatial variability of air quality and deposition within the National Parks and will be compared with modelled data. Effects on aquatic ecosystems are being assessed applying the methodology of CLRTAP ICP-Waters in all the National Parks with suitable locations, including the Canary Islands. Preliminary results will be presented discussing representativeness of current monitoring stations, monitoring techniques available in remote locations and the modelling analyses required.

Despite the increasing time sensitivity of climate change, many cities worldwide still heavily rely on coal. The extraction, processing, transport, and usage of coal lead to deteriorated air quality, resulting in complex environmental and public health problems for the local communities. Mapping different pollution sources in coal-centric cities is not trivial due to the hyperlocal nature of air pollution and the often low-density network of air quality monitors. This study explores the air quality issues surrounding coal-centric cities using a combination of qualitative and quantitative data from reference-grade air quality monitors, low-cost sensors (LCSs) deployed on citizens’ vehicles, and community engagement activities. It explores how LCSs can be used to characterize air quality at a high spatio-temporal resolution and how this information can be used to decode people’s perceptions of air quality issues and elicit local knowledge. We evaluated our approach in Sparwood (Canada), and Oskemen (Kazakhstan) which are very different cities, but are both heavily dependent on coal. LCSs have been proven an efficient tool to identify pollution hotspots that traditional reference monitors miss, while workshop-based activities making use of data maps and coding tools have successfully elicited information about pollution sources from non-experts, helping collaborative sense-making and informing new LCS deployment strategies. Understanding air quality in coal-centric cities as a complex socio-technical phenomenon can enable the coal industry, city officials, and residents to engage in addressing air quality issues.

Currently, the predominant methodology for assessing air quality in Thailand entails the deployment of air quality monitoring stations. In particular, EGAT's power facilities presently employ such stations for air quality assessment. The use of drones for air quality measurements reduces the variability of measurements and can determine the primary source of the air pollution. Therefore, the air quality monitoring system by unmanned aerial vehicles or drones will be a guideline that can be used to effectively reduce the time and cost of air quality monitoring in and around EGAT's power plants. The objective of this endeavor is to engineer a drone system integrated with specialized sensors for the purpose of real-time monitoring and data acquisition of air quality parameters, with results seamlessly relayed to an Internet of Things (IoT) platform. Applying drone is successful to implement air quality monitoring which parameters are PM2.5 and PM10. This drone can monitor particulate matter at both PM2.5 and PM10 concentration while this monitoring data is recorded and sent to an IOT platform, called Things.egat.co.th platform. In this experiment, the result shows that the air quality data from drone is comparable with the data of EGAT’s air quality mobile station. The results of PM2.5 and PM10 concentration were real-time displayed in the dashboard as graphs. This graphical interface facilitates comparisons over time, accessible via mobile phones or computers with uninterrupted internet connectivity. The average relative errors for PM concentrations measured by the cost-effective air quality monitoring drone systems stand at 7%, signifying a reliable means for monitoring particulate matter within an atmospheric context. It is therefore, established that this advancement in drone technology for Air Quality Monitoring attains the status of low-cost prototype of particulate matter monitoring drone system tailored for EGAT.

Scientific debate on both the cause and impacts of air pollution in the 1970s was a heated debate both within and between countries. The international scientific network was enhanced following the 1972 Stockholm Conference on the Human Environment, from which it was recommended that the WHO, assist governments in monitoring air pollution in terms of risk to health. Much of the scientific knowledge for the Long Range Transboundary Air pollution (LRTAP) and its subsequent Protocols comes from the Programme for the Evaluation and Monitoring of Long Range Pollutants in Europe (EMEP). The obligations of EMEP are to provide agreed upon tables and calculations showing annual trans-boundary transmissions of pollutants. The importance of international co-operation in the scientific study of climatic change was reiterated by the EEC and a number of American Science reports including the National Research Council and the National Academy of Science reports.Keywords: 1972 Stockholm Conference; air pollution; EMEP; international scientific network; Long Range Transboundary Air pollution (LRTAP)

Entering the 21st century, the whole world is paying much attention to environmental issues. In the past, environmental problems were regarded as national laws and policies or subjects of discussion by experts, but as the general public gradually began to feel environmental problems, it became a problem for all levels of the world to seek solutions. As a representative example, disasters such as floods and droughts have occurred all over the world due to the impact of the climate crisis, and many people are suffering damage, which also affects crops and fruit yields, resulting in economic problems. In Korea, record- breaking heavy rains fell in 2022, and as many people began to suffer damage, it became an opportunity to have a lot of interest in environmental issues. Another problem is that of fine dust. Fine dust refers to extremely small particles floating in the air in a solid or liquid state. Fine dust originally began to be managed comprehensively as one of the elements of air quality pollution. It began to be separately regulated and managed through the Special Act on Dust Reduction and Management”. Fine dust meant particles of 10 μm or less, but since the smaller the fine dust, the greater the damage, the ultra-fine dust of 2.5 μm or less was separately defined and started to be managed. Fine dust was also a field that was not of great interest unless those involved were involved, but around 2018, many articles on the effect of fine dust were published, and poor air quality was observed every day, The general public has come to regard fine dust as a serious problem. As a result, the demand for air purifiers among home appliances continues to rise due to the influence of fine dust, and interest in KF masks has also increased due to the corona crisis. In Korea, fine dust is managed through so-called ‘Eight Fine Dust Acts’. They are the ｢Special Act on the Reduction and Management of Fine Dust｣, ｢Indoor Air Quality Control Act｣, ｢Special Act On The Improvement Of Air Quality In Air Control Zones｣, ｢Clean Air Conservation Act｣, ｢School Health Act｣, ｢Framework Act On The Management Of Disasters And Safety｣, ｢Safety Control And Business Of Liquefied Petroleum Gas Act｣, ｢Special Act On The Improvement Of Air Quality In Port Areas｣ However, due to the nature of fine dust spreading through the air, it is difficult to say that it is a problem unique to one country and must be resolved through international cooperation. In consideration of this point, matters regarding international cooperation have been added to fine dust-related laws in Korea, but this is still a matter that requires further discussion. In the European Union, there are laws related to fine dust such as ｢European Climate Act｣, European Green Deal, National Emission Ceiling Directive, Ambient Air Quality Directive, Clean Air Policy Package for Europe, Directive on Ambient Air Quality and Cleaner Air for Europe. The European Union manages fine dust through these air quality guidelines. The European Union has legal characteristics that can manage a wide scale due to its structure, and countries outside the EU are also integrated through the ‘Convention on Long-Range Transboundary Air Pollution’. Considering the characteristics of the fine dust problem that occurs over a wide range, Korea also needs to consider the characteristics of the European Union's EU-scale guidelines and matters related to international agreements.

Transboundary pollution is part of air pollution originating from other countries has an impact on areas that are under the jurisdiction of other countries, The seasonal haze affected the health quality of ASEAN, it is evident that every time a forest fire occurs, the population with respiratory problems increases, including psychological stress. The objective of this paper is to investigate the problems and challenges that ATHP faces. It elaborates on the factors that contributed to LRTAP's relative success. It also analyzes and describes the measures taken in relation to the ATHP and compares its efficacy to LRTAP. The study used empirical-normative research method sourcing from literatures and journals. The study shows that ASEAN formed the Agreement on Transboundary Haze Pollution (ATHP) which has the aim of being a body that works to reduce and suppress air pollution in the ASEAN region, framed within the 1979 Convention on Long-Range Transboundary Air Pollution (LRTAP). In comparison to LRTAP, the aforementioned can be offered as a means of recommendation for the success of the AATHP. It is measurable that the importance placed on contribution, cooperation, scrutiny, democracy, and transparency in the agreement was a contributing factor in LRTAP's success.