Air Pollution Research Articles

Air Quality Prediction Using a U-Net Inspired 1D-CNN with Attention Mechanisms

<p style="line-height:150%;text-align:justify;text-indent:36.0pt;">Air quality prediction is crucial for environmental monitoring and public health. This work presents a new prediction model for air quality prediction using a one-dimensional convolutional neural network (1D- CNN). The proposed model resembles U-Net architecture which is used in image segmentation tasks popularly. The proposed model uses the strength of U-Net’s encoder-decoder design to capture both local and global features effectively. To enhance the model's performance, we integrate attention mechanisms to focus on the most relevant features. In addition, the self-calibrated convolutions are applied to adjust the convolutional filters to improve feature representation. The parameters of the proposed model are fine-tuned using the Frilled Lizard Optimization (FLO) <span> </span>algorithm for optimal performance. Experimental results show that the proposed model significantly outperforms traditional methods in terms of validation loss and Mean Square Error rates.</p>

Verification and Usability of Indoor Air Quality Monitoring Tools in the Framework of Health-Related Studies

Indoor air quality (IAQ) significantly impacts human health, particularly in enclosed spaces where people spend most of their time. This study evaluates the performance of low-cost IAQ sensors, focusing on their ability to measure carbon dioxide (CO2) and particulate matter (PM) under real-world conditions. Measurements provided by these sensors were verified against calibrated reference equipment. The study utilized two commercial devices from inBiot and Kaiterra, comparing their outputs to a reference sensor across a range of CO2 concentrations (500–1200 ppm) and environmental conditions (21–25 °C, 27–92% RH). Data were analyzed for relative error, temporal stability, and reproducibility. Results indicate strong correlation between low-cost sensors (LCSs) and the reference sensor at lower CO2 concentrations, with minor deviations at higher levels. Environmental conditions had minimal impact on sensor performance, highlighting robustness to temperature and humidity within the tested ranges. For PM measurements, low-cost sensors effectively tracked trends, but inaccuracies increased with particle concentration. Overall, these findings support the feasibility of using low-cost sensors for non-critical IAQ monitoring, offering an affordable alternative for tracking CO2 and PM trends. Additionally, LCSs can assess long-term exposure to contaminants, providing insights into potential health risks and useful information for non-expert users.

Unveiling sectoral coupling for resilient electrification of the transportation sector

Electrifying the transportation sector is crucial for reducing greenhouse gas emissions and offers numerous benefits including increased energy efficiency, lower total ownership costs, enhanced national energy security, and improved air quality. Despite the availability of necessary technologies, fully integrating the transportation and electricity sectors presents challenges in understanding all benefits and risks. Previous studies have not highlighted the role of coupling between these sectors. To better understand this coupling, this work reviews the structure of the current fossil-fuel-based transportation sector (including its dependence on the electricity sector) and case studies of its vulnerabilities to key risks. By adopting a systemic perspective, we uncover the indispensable interplay between the transportation and electricity sectors, shedding light on previously neglected dynamics. Leveraging the principles of grid architecture (GA), we introduce a hierarchical approach to assess vulnerabilities within the prevailing fuel-based transportation system and elucidate pathways for enhancement through electrification.

Climate response to stratospheric aerosol injection during the Harmattan season in West Africa

Abstract Stratospheric Aerosol Injection (SAI), a proposed climate intervention, aims to reduce the amount of solar radiation reaching the Earth's surface by increasing the reflectivity of the atmosphere, thereby offsetting the warming effect of greenhouse gases. During the Harmattan season (December-February) in West Africa, a natural meteorological phenomenon injects dust and sand particles into the atmosphere, leading to a cooling effect. In this study, we investigate the influence of SAI on West African surface temperature, dust, and other meteorological variables using the Whole Atmosphere Community Climate Model (WACCM) under the Shared Socioeconomic Pathway 2-4.5 (SSP2-4.5) scenario and the Assessing Responses and Impacts of Solar Climate Intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) dataset. Our findings indicate that SAI intervention significantly impacts the projected surface temperatures, specific humidity, and wind speed changes during the Harmattan season. Compared to a future without SAI, the intervention shows a significant net cooling effect over most parts of West Africa during the mid-future period (2050-2069). Also, SAI intervention significantly decreases moisture content over southern and northern West Africa in the near-future (2035-2054), mainly due to the net cooling effects over West Africa, when compared to a future without SAI. This feature is enhanced in the mid-future period. The cooling effects of SAI are likely to reduce the air's capacity to hold moisture, leading to lower specific humidity levels relative to a future without SAI. It could also have negative implications, such as increased aridity compared to a future without SAI in the northern and central regions of West Africa. These findings also highlight the potential for SAI to improve air quality in certain areas but also underscore the need for careful consideration of implementation strategies and possible trade-offs. The changes from SAI observed are specific to the ARISE simulation and may differ from other SAI simulations.

Analyzing Urban Air Quality Perceptions: Integrating Socio-Demographic Patterns with Sensor-Based Measurements Using Regression Model and Multidimensional Scaling

In this study, the urban air quality perceptions are explored in the metropolitan area of Târgu Mureș, Romania, emphasizing the interaction between socio-demographic factors, air quality measures, and industrial activity. The research addresses the need to understand how public perceptions align with objective air quality data and industrial influences, aiming to support sustainable urban planning. Data were gathered through a structured survey of 321 respondents and complemented by air quality measurements, including PM2.5 and PM10, and industrial production data. Statistical analyses, such as regression models and multidimensional scaling (PROXSCAL), were applied to identify patterns and relationships between socio-demographic characteristics, perceived air quality, and environmental factors. The results reveal significant links between demographic factors (e.g., age, awareness of local initiatives) and perceptions of air quality, alongside a nuanced interaction between air quality indicators and industrial activity. Respondents frequently identified traffic and industrial emissions as major contributors to air pollution, which was corroborated by sensor data trends. The findings underline the importance of integrating public perceptions with empirical data to design targeted policies and foster community engagement. This comprehensive approach provides actionable insights for improving urban air quality and advancing sustainable practices in mid-sized cities like Târgu Mureș.

A Review of Air Pollution Research in Panzhihua City from 2018 to 2023

Air pollution is a critical issue of concern to society, and researches on this topic has notably increased in recent years. Panzhihua City, a key resource-consuming and heavy industrial city in the southwestern region of China, places air pollution prevention and control at the top of its priorities. However, there is currently a lack of comprehensive reviews on air pollution studies specific to Panzhihua. This paper reviews the research conducted on air pollution and its control methods in Panzhihua City between 2018 and 2023. Ten relevant studies were included, the pollution levels of heavy metals and mineral particles in atmospheric dustfall, PM2.5, PM10, SO2, CO, NO2, and O3 were summarized. The paper also outlined the trends, potential causes of air pollution in Panzhihua, and suggested control measures, aiming to provide a reference for future research and offer a scientific basis for local government policy-making regarding air pollution control

A quantitative analysis of level of sustainability disclosure in corporate annual reports of Indian companies

The importance of corporate sustainability disclosure is growing because of social and environmental concerns. Rapid urbanization and industrialization in India have caused serious problems with the quality of air and water. This study examines the level of sustainability disclosure in the annual reports of Indian companies, focusing on environmental, social, and economic commitments in line with frameworks like GRI and BRSR-SEBI. The objective of the study was to examine the degree to which businesses in India publish information about their sustainability policies and efforts in their annual reports. A multistage sampling technique was used to sample the top 50 Indian companies covering the 2021–2022 fiscal year from 11 sectors. Descriptive and content data analysis techniques were employed using mean, median, and standard deviation. Findings revealed that the Global Reporting Initiative (GRI) is the most widely adopted framework, followed by the International Integrated Reporting Council (IIRC). Furthermore, almost all the companies practiced environmental disclosure, particularly greenhouse gas emissions and climate impact and energy usage. The study concluded that sustainability disclosure varies across different sectors and Indian corporations. The study recommended strengthening the adoption of standardized reporting frameworks like GRI, IIRC, and BRSR-SEBI through regulatory incentives or rules that emphasize their benefits. Furthermore, it was recommended that Indian companies should include external certifications and recognitions in their annual reports to demonstrate their commitment to sustainability and enhance the credibility and transparency of their sustainability reporting.

Factors influencing spatiotemporal variability of NO2 concentration in urban area: a GIS and remote sensing-based approach.

The growing global attention on urban air quality underscores the need to understand the spatiotemporal dynamics of nitrogen dioxide (NO2) and its environmental and anthropogenic factors, particularly in cities like Dhaka (Gazipur), Bangladesh, which suffers from some of the world's worst air quality. This study analysed NO2 concentrations in Gazipur from 2019 to 2022 using Sentinel-5P TROPOMI data on the Google Earth Engine (GEE) platform. Correlations and regression analysis were done between NO2 levels and various environmental factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), land use and land cover (LULC), population density, road density, settlement density, and industry density. The results reveal significant seasonal variations. The highest annual meanNO2 concentration (3.1 × 10-4mol/ m2)was recorded for winter 2021, and the lowest (1.1 × 10-4mol/m2) was for monsoon 2022. The study demonstrates a significant positive correlation between NO2 concentrations and LST (0.47), road density (0.55), settlement density (0.44), and industrial density (0.35) and a negative correlation with NDVI (- 0.4). Regression analysis revealed that NO2 concentrations were positively associated with land surface temperature (LST; β = 0.02, R2 = 0.22), road density (β = 0.002, R2 = 0.30), settlement density (β = 0.002, R2 = 0.19), and industrial density (β = 0.007, R2 = 0.12), while a negative association was observed with NDVI (β = - 0.28, R2 = 0.16). This research offers critical insights for policymakers and urban planners, advocating for enhanced green infrastructure, stringent emission controls, and sustainable urban development strategies to mitigate air pollution in Gazipur. Our methodological approach and findings contribute to the broader discourse on urban air quality management in developing countries.

Salutogenic Effects of Greenspace Exposure: An Integrated Biopsychological Perspective on Stress Regulation, Mental and Physical Health in the Urban Population

Abstract Globally, urbanization is associated with increased risk for physical and mental diseases. Among other factors, urban stressors (e.g. air pollution) are linked to these increased health risks (e.g. chronic respiratory diseases, depression). Emerging evidence indicates substantial health benefits of exposure to greenspaces in urban populations. However, there is a need for an overarching framework summarizing the plausible underlying biological factors linked to this effect, especially within the context of stress regulation. Therefore, by outlining the effects of greenspace exposure on stress parameters such as allostatic load, oxidative stress, mitochondria, and the microbiome, we conceptualize an integrated biopsychological framework to advance research into the salutogenic and stress-regulatory potential of greenspace exposure. In addition, we discuss the understudied potential health benefits of biogenic volatile organic compounds. Our perspective highlights the potential for innovative greenspace-based interventions to target stress reduction, and their prospect as add-ons to current psychotherapies to promote mental and physical health in urban populations.

Analysis of the Ecological and Economic Benefits of Electric Buses in Sustainable Urban Transportation

This study focuses on the importance of sustainable urban transportation, particularly the key role of electric buses in addressing global challenges such as air pollution and climate change. By selecting a metropolitan area with a population of at least 500,000 as a case study, we constructed a mathematical model to assist cities in understanding the ecological impact of transitioning to a fully electric bus fleet. The model takes into account various factors, including reduced exhaust emissions, improved air quality, reduced carbon emissions, and the operating costs and charging infrastructure development costs associated with electric buses. On the financial side, the model also analyzes the economic impact of transitioning from traditional fuel-powered buses to electric buses, considering potential external funding sources such as government subsidies and infrastructure investments to alleviate the citys financial burden. Ultimately, we developed a 10-year road-map to assist city transportation authorities in planning the renewal and expansion of their electric bus fleets. Through this letter, we emphasize the environmental and economic advantages of electric buses and encourage city transportation officials to actively consider electric buses as part of sustainable urban transportation, providing our research findings as a basis for decision-making.

Microbiological quality of the air in the area of influence of the former wastewater treatment plant in Cajamarca, Peru

It has been determined that there is air pollution within wastewater treatment plants and their surrounding areas. This impacts the health of nearby populations. Therefore, this research aimed to evaluate the air’s microbiological quality in the area of influence of the former Wastewater Treatment Plant (WWTP) in the city of Cajamarca. Microbiological air sampling was conducted during the rainy and dry seasons using the RCS Standard Hycon air sampler, planting on tryptic soy agar medium to determine the total count of all microorganisms, bacteria, and fungi separately. OSHA (Occupational Safety and Health Administration), PN-89/Z-04111/02, and PN-89/Z-04111/03 were the standards used to determine the air quality of the study area since Peru currently does not have such regulation standards. The highest values obtained were 4560 CFU/m³ and 4360 CFU/m³ during the dry season. Sixteen concentrations of the total microorganism count exceeded the limits established by OSHA standards, and ten concentrations of bacteria exceeded the Polish standard PN-89/Z-04111/02. Although the concentrations of fungi were high, they did not exceed any reference limits. We determined a positive correlation between the concentration of microorganisms and the relative humidity, with a maximum of 85.67% humidity during the dry season. Additionally, a negative correlation with wind speed was found, with values ranging from 0.37 m/s to 2.58 m/s during the dry season and from 0.37 m/s to 1.87 m/s during the rainy season. Genera of public health importance such as Staphylococcus and Penicillium were identified. Finally, a survey was conducted among the nearby population to assess the impact on public health. And, it was established that the health impact on the surrounding populations is mainly low.

Using low-cost sensors to assess common air pollution sources across multiple residences

The rapid development of low-cost sensors provides the opportunity to greatly advance the scope and extent of monitoring of indoor air pollution. In this study, calibrated particle matter (PM) sensors and a non-negative matrix factorisation (NMF) source apportionment technique are used to investigate PM concentrations and source contributions across three households in an urban residential area. The NMF is applied to combined data from all houses to generate source profiles that can be used to understand how PM source characteristics are similar or differ between different households in the same urban area. PM2.5 and PM10 concentrations in all three houses were greater, more variable, and significantly different to ambient concentrations recorded at a nearby ambient monitoring site. Concentrations were also significantly different between houses, with the World Health Organisation 24-h guideline limits for PM2.5 breached in one household. The applied methodology was highly successful at modelling concentrations for all the houses (R2≥ 0.983), finding that across the houses the I/O (indoor to outdoor sources ratio) was the lowest for PM1 (down to 0.08), and greatest for PM10 (up to 4.93). Whilst the sources could not be clearly distinguished further than being outdoors or indoors, the methodology provides clear insights to source variability within and between the monitored houses. These results highlight the importance of monitoring indoor air pollution to improve pollution exposure estimates, as whilst people may live in areas with acceptable ambient air quality, they can be exposed to unhealthy concentrations in their own homes. This method may be applied in future studies for extended periods to investigate the influence of source seasonality on PM concentrations or scaled up to investigate source variability across larger geographical areas.

Chemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics.

Direct measurement of brake wear particles from a light-duty vehicle under real-world driving conditions

Abstract As tailpipe emissions have decreased, there is a growing focus on the relative contribution of non-exhaust sources of vehicle emissions. Addressing these emissions is key to better evaluating and reducing vehicles’ impact on air quality and public health. Tailoring solutions for different non-exhaust sources, including brake emissions, is essential for achieving sustainable mobility. Studying emissions from vehicles in real-world scenarios provides a better understanding of their environmental impact compared to laboratory testing alone. This study presents findings on the direct measurement of brake particles and the characterization of this source of particulate matter in real-world conditions using a mobile laboratory. In situ measurements of particle concentration and size distribution showed good agreement with previous laboratory studies, indicating the suitability of the approach to investigate break particle emissions during real-world operation. The study demonstrates that particle size distributions can vary based on the temperature of the brake disk, which is influenced by the initial braking speed, with significant variations observed between speeds of 60, 80, 100, and 120 km/h. Particles with sizes between 6 and 523 nm were released into the air from the brake system, although it is likely that larger particles were also emitted but not captured due to the upper detection limit of the Engine Exhaust Particle Sizer. During harsh braking events, such as decelerations of 4.2 m/s2 from 120 km/h, a concentration of up 106 (#/cm3) was measured for particles under 8 nm. Moreover, scanning electron microscope analysis revealed that nanoparticles are present in the form of agglomerates, whose shape can change depending on the formation process. Elements present in the particles comprised mainly iron, copper, and aluminium, indicating wear of the brake pad materials and disk components.

Ecosystem services linked to nature-based solutions for resilient and sustainable cities in India

Rampant urbanization and undervaluing of the natural ecosystem have detrimental impacts on urban spaces – increased flooding risk, increased air and water pollution, water stress, resource inefficiency, loss of biodiversity, and increased risk of ill health. Climate change further exacerbates the adverse impacts of urbanization. Despite the importance of the natural ecosystem, the blue and green spaces of the cities in India have drastically decreased. The present study highlights the degrading natural ecosystem, the negative impacts, and the need for resilience in Indian cities. Eco-centric approaches like nature-based solutions (NBS) are closely related to sustainability and resilience, offering a more efficient and cost-effective approach to urban development than traditional approaches. The paper explores the concept of NBS, focusing on ecosystem services as a ‘living’ and ‘adaptable’ tool to make cities resilient and sustainable with many regional implementations. It also focuses on the role of NBS in achieving the United Nations’ Sustainable Development Goals (SDGs). The paper critically analyses the five notable NBS projects from different countries (USA, Canada, The Netherlands, China, and Australia) and further addresses the viabilities for NBS intervention in Indian cities. It is observed that the successful adaptation of NBS in urban development necessitates eco-centric policies, collaborative research, adaptive management practices, community engagement, and a strong emphasis on a multi-benefit approach. A proactive focus on ecosystem services is strongly recommended for Indian cities, which includes raising an understanding of the value of nature, introducing NBS at the planning stage, and encouraging investment in ecosystem-based approaches.

Leading the Way: Working Towards Heritage-Led Nature Recovery in Natural England

ABSTRACT The climate and biodiversity crises are an immediate and existential threat, risking the widespread loss and degradation of habitats across England. The UK Government’s Environmental Improvement Plan commits us to being the first generation to leave the environment in a better state than we found it, and the long-term conservation and enhancement of the historic environment is an essential component of this commitment. However, as the pressure mounts to deliver nature recovery faster, at greater scale and within increasingly tighter budgets, does heritage run the risk of being overlooked? Natural England’s 2023 guidance Nature Recovery and the Historic Environment provides a best-practice framework to ensure appropriate integration of heritage into nature recovery, but it also goes further – exploring the correlations between our historic and natural environments and the ways in which heritage assets can provide added value to nature recovery projects. This paper will take this thinking a step further and consider how projects led by heritage can deliver across the full spectrum of environmental objectives: to inform, inspire and improve habitat restoration, species abundance, soil health, improved air and water quality, public access, landscape distinctiveness and rural placemaking.

Rural Roads to cognitive Resilience (RRR): A prospective cohort study protocol.

Ambient air pollution, detrimental built and social environments, social isolation (SI), low socioeconomic status (SES), and rural (versus urban) residence have been associated with cognitive decline and risk of Alzheimer's disease and related dementias (ADRD). Research is needed to investigate the influence of ambient air pollution and built and social environments on SI and cognitive decline among rural, disadvantaged, ethnic minority communities. To address this gap, this cohort study will recruit an ethnoracially diverse, rural Florida sample in geographic proximity to seasonal agricultural burning. We will (1) examine contributions of smoke-related fine particulate matter (PM2.5) exposures to SI and cognitive function; (2) determine effects of built and social environments on SI and cognitive function; and (3) contextualize SI and cognitive function among residents from different ethnoracial groups during burn and non-burn seasons. We will recruit 1,087 community-dwelling, dementia-free, ≥45-year-olds from five communities in Florida's Lake Okeechobee region. Over 36 months, participants will complete baseline visits to collect demographics, health history, and health measurements (e.g., blood pressure, body mass index) and 6-month follow-ups assessing cognitive function and social isolation at each visit. A subsample of 120 participants representative of each community will wear smartwatches to collect sensor data (e.g., heart rate) and daily routine and predefined activities (e.g., GPS-captured travel, frequent destinations) over two months. Ecological momentary assessments (EMA) (e.g., whether smoke has bothered participant in last 30 minutes) will occur over two months during agricultural burning and non-burning months. PurpleAir monitors (36 total) will be installed in each community to continuously monitor outdoor PM2.5 levels. We expect to identify individual- and community-level factors that increase the risk for SI and cognitive decline in a vulnerable rural population.

Performance and emission characteristics of domestic LPG gas burners with hydrogen integration

Abstract Domestic gas burners are a significant source of indoor air pollution and contribute substantially to household energy consumption. While transitioning to carbon-free fuels, such as hydrogen, presents considerable safety challenges, blending hydrogen with conventional fuels can improve combustion efficiency and reduce emissions. However, factors like the differences in the Wobbe index, loading height, power input, and fuel blending complicate understanding the aerated burner performance. This is particularly relevant for large establishments like restaurants and canteens, where hydrogen blending can have a notable impact. This study evaluates four burner designs—commercial (A), straight holes (B), slots (C), and swirl (D)—across thermal inputs (0.8–1.6 kW), with a focus on the swirl burner using hydrogen blends (0%, 34%, and 52% by volume) and varying loading heights (5 mm, 10 mm, and 15 mm). Straight hole (B) and swirl (D) burners were 3D-printed using stainless steel metal powder, while the slot (C) burner was machined. Key performance metrics such as flame behavior, thermal efficiency, pressure drop, and CO emissions were analyzed. Improving primary aeration by reducing flow resistance and enhancing secondary aeration through swirl action resulted in better flame-load interaction and minimized heat loss. The slot burner (C) achieved a thermal efficiency of 63.5%, while the swirl burner (D) reached 65% efficiency at a 10 mm height. The swirl burner maintained high efficiency with up to 52% hydrogen by volume across a wide range of operational conditions, with some exceptions. Additionally, the use of hydrogen eliminated soot emissions. Optimal performance occurred at a 10 mm loading height, with stable efficiency and lower emissions across various power inputs and hydrogen blends. The complex interactions between primary aeration, secondary mixing, and flame-load dynamics during hydrogen blending require thorough evaluation before incorporating hydrogen into aerated LPG burners.

An interlaboratory comparison to quantify oxidative potential measurement in aerosol particles: challenges and recommendations for harmonisation

Abstract. This paper presents the findings from a collaborative interlaboratory comparison exercise designed to assess oxidative potential (OP) measurements conducted by 20 laboratories worldwide. This study represents an innovative effort as the first exercise specifically aimed at harmonising this type of OP assay, setting a new benchmark in the field. Over the last decade, there has been a noticeable increase in OP studies, with numerous research groups investigating the effects of exposure to air pollution particles through the evaluation of OP levels. However, the absence of standardised methods for OP measurements has resulted in variability in results across different groups, rendering meaningful comparisons challenging. To address this issue, this study engages in an international effort to compare OP measurements using a simplified method (with a dithiothreitol (DTT) assay). Here, we quantify the OP in liquid samples to focus on the protocol measurement itself, while future international OP interlaboratory comparisons (ILCs) should aim to assess the whole chain process, including the sample extraction. We analyse the similarities and discrepancies observed in the results, identifying the critical parameters (such as the instrument used, the use of a simplified protocol, the delivery and analysis time) that could influence OP measurements and provide recommendations for future studies and interlaboratory comparisons even if other crucial aspects, such as sampling PM methods, sample storage, extraction methods and conditions, and the evaluation of other OP assays, still need to be standardised. This collaborative approach enhances the robustness of the OP DTT assay and paves the way for future studies to build on a unified framework. This pioneering work concludes that interlaboratory comparisons provide essential insights into the OP metric and are crucial to move toward the harmonisation of OP measurements.

Emissions of Biogenic Volatile Organic Compounds from Plants: Impacts of Air Pollutants and Environmental Variables

Emissions of Biogenic Volatile Organic Compounds from Plants: Impacts of Air Pollutants and Environmental Variables