Monitoring Stations Research Articles

A prospective study on the cardiorespiratory effects of air pollution among residents of the Tibetan Plateau

The Tibetan Plateau is characterized by high ozone concentration which poses a significant public health concern. However, the causal evidence linking ozone pollution to adverse cardiopulmonary health impacts, as well as the understanding of its underlying biological mechanisms, remains limited. Additionally, exposure levels to particulate and other gaseous air pollutants along with their associated health effects in this region are largely unknown. To address these gaps, we conducted a prospective follow-up study in Tibet from May 2021 to November 2021. In consideration of the potential synergistic effects of chronic hypobaric hypoxia, two Tibetan cities with different altitudes, Lhasa (3650 m) and Nyingchi (3000 m), were chosen to implement atmospheric monitoring and health measurement. We employed cutting-edge, high-precision instruments at stationary monitoring sites to measure ambient air pollution and collected particle samples. Portable devices were used to monitor personal exposure levels of ozone and black carbon. A total of 212 healthy Tibetan college students participated in up to four clinical visits, yielding 774 visits in total, during which functional endpoints were measured and biological samples were collected. The primary aim of this study is to evaluate the cardiorespiratory effects of ambient ozone under hypoxic conditions, where its impact may be amplified due to the region's unique environmental characteristics. The secondary aim is to provide a comprehensive assessment of other air pollutants, including their exposure levels, sources, and health effects. By addressing these aims, the study offers valuable insights into air quality and its health implications in this unique high-altitude setting. This paper outlines the research motivation, measurement framework, and preliminary findings.

Study of patterns of PM2.5 and PM10 changes in the atmospheric air of Ivano-Frankivsk region

The purpose of this research is to analyse the temporal patterns of changes in concentrations of dust particles – aerosols, regression modeling of the interdependence of PM2.5 and PM10 at the level of instantaneous, average hourly, average daily and average weekly concentrations, and assessment of the impact of the anthropogenic component of dust pollution of atmospheric air in the Ivano-Frankivsk region on regularities of the temporal distribution of these shares.The database for the study included measurements of the concentration of PM10, PM2.5 every hour at four Ecocity public monitoring stations: in the central part of the city of Ivano-Frankivsk, in the village of Bovshiv within the influence of the Burshtyn TPP, in the village of Broshniv-Osada within the influence of the woodworking enterprise "SVISS KRONO" and within the recreational territory of the village of Mykulychyn, Nadvirnyan district. The general regularity of the temporal distribution of PM10, PM2.5 was established for all posts for daily fluctuations – the highest concentrations are observed at night, the lowest – during the period of maximum daily temperatures. It has been proven by actual data that the greater the level of atmospheric air pollution (the greater the anthropogenic component of PM), the higher the daily concentrations of PM10, PM2.5 and the more often the one-time standards of solid suspended particles are exceeded.According to the data of monitoring stations from the territories with different anthropogenic influence, functional dependences of PM2.5 content on PM10 content were obtained for instantaneous values, hourly average values, daily average values, and weekly average values. The scientific novelty of the conducted research consists in the establishment of identical functional interdependence of temporal changes in concentrations of PM2.5 and PM10 dust particles within a conditionally clean area and within the limits of the influence of stationary sources of pollution with a high level of direct regression dependence and a coefficient of determination greater than 0.9 in all cases. This allows us to conclude that the temporal patterns of changes in both PM2.5 and PM10 will not differ in clean and polluted air conditions for the Ivano-Frankivsk region. In conditions of incomplete data on the temporal distribution of the concentration of PM10, the obtained equations can be used to forecast the temporal distribution of the concentration of PM2.5.

INTEGRATED MODEL FOR FORECASTING TIME SERIES OF ENVIRONMENTAL POLLUTION PARAMETERS

The quality of life in large urban areas is considerably diminished by air pollution, with major contributors being motor vehicles, industrial activities, and fossil fuel combustion. A major contributor to air pollution is coal-fired and thermal power plants, which are commonly found in emerging markets. In Astana, Kazakhstan, a rapidly expanding city's significant reliance on coal for heating and considerable building exacerbate air pollution. This research is essential for improving urban development practices that support sustainable growth in rapidly expanding cities. Using time series data from four monitoring stations in Astana using fractal R/S analysis, the study looks at long-term patterns in air pollutant levels, especially PM10 and PM2.5. The stations' Hurst exponents were determined to be 0.723, 0.548, 0.442, and 0.462. Additionally, the flow window method was used to study the Hurst exponent's dynamic behavior. The findings showed that one station's pollution levels had long-term memory, which suggests that the time series is persistent. While anti-persistence was noted in the third and fourth sites, data from the second station indicated nearly random behavior. The Hurst exponent values explain the October 2021 spike in pollution levels, which is probably caused by thermal power plants close to the city. The fractal analysis of time series could serve as an indicator of environmental conditions in a given region, with persistent pollution trends potentially aiding in predicting critical pollution events. Anti-persistence or temporary pollution spikes may be influenced by the observation station's proximity to pollution sources. Overall, the findings suggest that fractal time series analysis can act as a valuable tool for monitoring environmental health in urban areas.

ANALYSIS AND ASSESSMENT OF AIR QUALITY IN ASTANA: COMPARISON OF POLLUTANT LEVELS AND THEIR IMPACT ON HEALTH

This study presents an in-depth analysis of air quality in Astana, Kazakhstan, utilizing both mobile and stationary air monitoring systems over a two-year period. The research focuses on tracking key air pollutants, namely carbon monoxide (CO), nitrogen dioxide (NO₂), particulate matter (PM2.5 and PM10), and sulfur dioxide (SO₂), providing a comparative assessment of seasonal trends and the sources of pollution, which include transportation, industrial emissions, and domestic heating during the cold season. The study emphasizes the significance of monitoring systems in urban environments to understand better the impact of air pollution on public health and the effectiveness of sustainable interventions. One of the major insights from this research is the comparison between seasonal variations in pollutant levels and the city's transition toward sustainable energy practices, such as increased gasification and the use of electric transportation, which has already demonstrated a positive impact on reducing emissions during peak heating periods. The results show that while Astana has improved air quality, air pollution remains a concern, especially in winter due to the increased use of solid fuel. This paper emphasizes the importance of real-time data from mobile sensors and suggests their wider use to complement stationary sensors for better monitoring. In addition to pollutant tracking, the study delves into the health implications of prolonged exposure to air pollutants, particularly in urban areas. The study concludes by advocating for expanded use of mobile monitoring systems and advanced data analytics to provide actionable insights for policymakers, urban planners, and public health officials.

Recent Trends in Air Pollution in the Most Important City of The Romanian Black Sea Coast

Air pollution is one of the greatest environmental issues of contemporary society, affecting virtually all major cities worldwide, including those in Romania. Consequently, evaluating the dynamics of pollution conditions in urban environments is essential for designing control measures and adaptation strategies to this environmental disruption. This paper aims to analyze the recent dynamics of pollution and air quality in Constanta, the largest city on the Romanian coastline and one of the most important cities along the entire Black Sea coast. The investigations were based on the official concentrations of major atmospheric pollutants recorded at air quality monitoring stations located within or near Constanta. The dynamics of pollutant concentrations were explored at annual and seasonal levels, and the results showed a mixed picture of changes in the atmospheric pollutant concentrations, in recent years. Essentially, the findings highlighted both increases and decreases in air pollution conditions, emphasizing the need for constant air quality monitoring and controlling throughout Constanta city.

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

ENVIRONMENTAL POLLUTION IN THE CITY OF BUCHAREST

The work covers air quality monitoring tools in Bucharest, major industrial facilities impacting pollution and the city's eight air quality monitoring stations. Data on pollutant emissions from Bucharest's thermal power plants (SO2, NOx and dust) are presented. Finally, the paper graphically presents the annual average concentrations of NO2, SO2, PM10 and O3 at the monitoring stations and examines pollution peaks and daily limit exceedances from 2017 to 2022.

Novel spatio-temporal attention causal convolutional neural network for multi-site PM2.5 prediction

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatio-temporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%–10%.

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO2, PM2.5, CO, and PM10, while the levels of O3 and SO2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O3. Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution.

A holistic approach to salinity intrusion vulnerability assessment using geospatial technologies: An application for mekong delta of vietnam

Saltwater intrusion is a critical natural hazard impacting millions of lives globally, particularly in the Mekong Delta, Vietnam. This study presents a comprehensive approach to assessing saltwater intrusion vulnerability, combining hazard, exposure/sensitivity, and adaptive capacity. We developed a salinity intrusion hazard map using data from 68 monitoring stations and 156 prevention culverts collected on February 23, 2020. Twenty-eight indicators representing exposure, sensitivity, and adaptive capacity were standardized and weighted using the Iyengar-Sudarshan method. Our analysis revealed that 15.74 % of the study area, particularly Mo Cay Bac and Mo Cay Nam districts in Ben Tre province and Dam Doi district in Ca Mau province, exhibits very high vulnerability to saltwater intrusion. These findings provide critical insights for designing effective saltwater intrusion warning systems and implementing targeted disaster risk management strategies in the Mekong Delta. The approach and results are also applicable to similar coastal deltas and regions worldwide, offering a valuable framework for mitigating saltwater intrusion impacts.

The Temporal and Spatial Analysis and Assessment of Water Quality in the Tuo River (Suzhou Section) based on the CCME-WQI Method

To investigate the spatiotemporal variations and disparities in water quality within the Suzhou section of the Tuo River, as well as to identify the key pollution factors and influencing elements, this study utilized monitoring data from five water quality monitoring stations along the Tuo River in Suzhou, collected between 2020 and 2022. After analyzing the spatiotemporal variations in water quality indicators, the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was employed to assess the river's water quality status. The study also examined the causes of water pollution and proposed appropriate pollution control measures. The research results indicate that:(1) Total Nitrogen (TN), Chemical Oxygen Demand (COD), Permanganate Index (CODMn), and Fluoride (F) were identified as the main pollution factors, with COD and TN being the primary pollutants exceeding standard limits, with exceedance rates of 55.66% and 37.14%, respectively;(2)From 2020 to 2022, the concentrations of CODMn, COD, and TN were significantly higher during the flood season compared to the non-flood season, suggesting that non-point source pollution is the major contributor to the pollution load;(3)Temporally, the study area exhibited significant seasonal variations in water quality, with water quality being better during the non-flood season than during the flood season. Spatially, the study area showed notable spatial differences in water quality, with the S1 monitoring station recording the poorest water quality, while the S3 station exhibited the best;(4)The water quality of the Tuo River was significantly influenced by precipitation, with increased rainfall during the flood season exacerbating urban runoff, agricultural non-point source pollution, and internal pollution within the river channels.

Gaussian modeling with B-splines for spatial functional data on irregular domains

Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats data as functions, providing a more comprehensive understanding of their behavior and variability. This approach is particularly beneficial for exploring observations collected over time or space. The Spatial Functional Data Analysis (SFDA) incorporates spatial locations as an additional dimension. This integration enhances the ability to model complex associations between curves in the functional domain. Despite progress in analyzing multivariate spatial data, limited attention has been given to SFDA. This study addresses this gap by proposing a Bayesian Gaussian functional approach based on B-splines. The proposal also addresses challenges posed by irregularly spaced observations in the functional domain, a feature rarely considered in the literature. A simulation study is developed to evaluate performance. Finally, results are explored using a relatively novel real dataset from monitoring stations of temperature in Mexico City.

Study on the characteristics of urban background ozone pollution based on long-term observations from mountain sites in China during 2015–2022

In recent years, Chinese cities have faced persistent O3 pollution challenges. O3 concentrations at urban mountain sites, serving as proxies for urban background O3 levels, can help address data gaps in O3 research in China. This study, for the first time, analyzed O3 data from ten urban mountain sites during 2015–2022 using statistical methods like the Mann-Kendall test, Theil-Sen slope estimation, and Pearson correlation analysis to reveal characteristics and trends in urban background O3 and proposed a metric for assessing mountain sites as urban background stations. Results indicate that O3 at mountain sites were generally higher than at plain sites, regardless of mean, 10th percentile, or 90th percentile the average maximum daily 8-h average (MDA8) O3. Although mountain sites exhibited similar annual and diurnal variations as plain sites, their amplitudes were significantly lower. Significant upward trends (P < 0.05) in MDA8 O3 were observed mainly at plain sites, whereas trends at mountain sites were insignificant (P > 0.05) during 2015–2022. The increase in urban ground-level O3 was largely due to changes in VOCs and NOx emission structures, which reduce nighttime titration effects while enhancing daytime O3 production. The insignificant trends observed at mountain sites suggest that these mountain sites may be in NOx-limited or transitional regimes rather than VOCs-limited regimes, which is also associated with their lower NOx concentrations compared to plain sites. These findings suggest that if China continues to prioritize the reduction of NOx emissions, urban ground-level O3 could further increase and approach the O3 at mountain sites. Finally, this study proposes that the correlation in nighttime O3 concentrations between mountain and plain stations can serve as a valuable indicator for evaluating the suitability of mountain stations as regional background stations in urban areas. It recommends mountain sites in cities such as Changji, Nanping for urban background O3 monitoring stations.

Weighted Averages and Polynomial Interpolation for PM2.5 Time Series Forecasting

This article describes a novel method for the multi-step forecasting of PM2.5 time series based on weighted averages and polynomial interpolation. Multi-step prediction models enable decision makers to build an understanding of longer future terms than the one-step-ahead prediction models, allowing for more timely decision-making. As the cases for this study, hourly data from three environmental monitoring stations from Ilo City in Southern Peru were selected. The results show average RMSEs of between 1.60 and 9.40 ug/m3 and average MAPEs of between 17.69% and 28.91%. Comparing the results with those derived using the presently implemented benchmark models (such as LSTM, BiLSTM, GRU, BiGRU, and LSTM-ATT) in different prediction horizons, in the majority of environmental monitoring stations, the proposed model outperformed them by between 2.40% and 17.49% in terms of the average MAPE derived. It is concluded that the proposed model constitutes a good alternative for multi-step PM2.5 time series forecasting, presenting similar and superior results to the benchmark models. Aside from the good results, one of the main advantages of the proposed model is that it requires fewer data in comparison with the benchmark models.

A New Method Proposed for the Estimation of Exposure to Atmospheric Pollution through the Analysis of Black Pigments on the Lung Surface

Megacities can be considered excellent laboratories for studying the effects of the urban environment on human health. Typically, exposure to pollution is estimated according to daily or annual averages of pollutant concentrations, collected at monitoring stations, using satellite data for remote sensing of pollutant levels, considering proximity to major roads, or through personal exposure monitoring with portable sensors. However, these approaches fall short in identifying individual exposure values over a lifetime. It is well established that individuals living in large urban areas inhale atmospheric particles containing carbonaceous components, resulting in the deposition of black pigments in lung tissue, known as black carbon or anthracosis. This study aims to detail the procedures for assessing the deposition of such pigments, which serve as an estimate of an individual’s exposure to atmospheric pollution particles. Data collection involves administering detailed questionnaires and capturing lung images in the autopsy room. The analysis is based on macroscopic quantification of black pigments, supplemented by an evaluation of personal habits and the clinical histories of the individuals. This method of estimating lifetime exposure to inhaled particles provides a valuable tool for understanding the correlation between urban living and its potential health effects.

Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data.

Air sensors can provide valuable non-regulatory and supplemental data as they can be affordably deployed in large numbers and stationed in remote areas far away from regulatory air monitoring stations. Air sensors have inherent limitations that are critical to understand before collecting and interpreting the data. Many of these limitations are mechanistic in nature, which will require technological advances. However, there are documented quality assurance (QA) methods to promote data quality. These include laboratory and field evaluation to quantitatively assess performance, the application of corrections to improve precision and accuracy, and active management of the condition or state of health of deployed air quality sensors. This paper summarizes perspectives presented at the U.S. Environmental Protection Agency's 2023 Air Sensors Quality Assurance Workshop (https://www.epa.gov/air-sensor-toolbox/quality-assurance-air-sensors#QAworkshop) by stakeholders (e.g., manufacturers, researchers, air agencies) and identifies the most pressing needs. These include QA protocols, streamlined data processing, improved total volatile organic compound (TVOC) data interpretation, development of speciated VOC sensors, and increased documentation of hardware and data handling. Community members using air sensors need training and resources, timely data, accessible QA approaches, and shared responsibility with other stakeholders. In addition to identifying the vital next steps, this work provides a set of common QA and QC actions aimed at improving and homogenizing air sensor QA that will allow stakeholders with varying fields and levels of expertise to effectively leverage air sensor data to protect human health.

Accuracy of estimating a human-sighted visual range for short-term particulate matter concentration in Chiang Mai, Thailand

Background: Haze crisis in the North Thailand has occurred more than decade. Although four air monitoring stations (Air4Thai) are installed in Chiang Mai, three stations are in the city. According to the California Department of Public Health, communities have used both measurement devices and visual estimates as the concentration of smog changes quickly. Therefore, particle levels are estimated from visual assessments or visual views as another measurement as well. This study aims to assess whether human-sighted visual range is reliable or not in Chiang Mai areas. Objectives: To determine accuracy of human-sighted visual range to estimating short-term Particulate Matter (PM) concentration Methods: A retrospective study was conducted purposively among hospitals and Public Health Office at 25 districts in Chiang Mai province where DustBoy air measurement devices were installed and reported air quality including PM2.5, PM10, temperature and relative humidity. The human-sighted visual range (Hr) were reported by trained health staff following the manual applied from “Wildfire Smoke, A Guide for Public Health Officials”. The Hr were reported into six color levels: blue, green, yellow, light orange, dark orange, and red., three times per day in morning (9.00), afternoon (13.00) and evening (16.00). Data was collected between April and May 2018. Both data were matched according to the date and time, excepted data with relative humidity exceeded 65% were excluded. Descriptive statistics described general data and Cohen's kappa statistics analyzed the accuracy of Hr. Results: Hr accuracy for PM10 was at a good level (79.21%) while Hr for PM2.5 was not useful (26.49%). Conclusion: Human-sighted visual range could be applicable for PM10 assessments for community. However, it would not be effective in early-morning, twilight-hours, rainy day, cloudy or humidity &gt;65%. Moreover, personal experience and judgment should be of concern.

Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001–2022)

Increasing trends in allergic respiratory diseases may be linked to climate changes affecting pollen levels. This study aimed to investigate the temporal trends in seasonal indexes of 9 allergenic pollen types in the Veneto Region, Northern Italy. Available daily data from 20 monitoring stations covered 2001–2022 for Corylaceae, Cupressaceae, Poaceae, Oleaceae, and Urticaceae (pollen families), and 2006–2022 for Alnus, Betula, Ambrosia, and Artemisia (pollen genera). The 95-percentage method was used to identify the start/end dates of pollen seasons annually, and the seasonal cumulative pollen concentration (Seasonal Pollen Integral, SPIn) was calculated. The non-parametric Theil-Sen median slope method was used to examine trends in pollen seasons’ start day, duration, and SPIn in the whole region and for climatic areas (alpine, subcontinental eastern, subcontinental western). The results showed a clear upward trend in SPIn for several pollens, with median slopes ranging from 326 (Alnus) to 1089 p/m3 per 10yrs (Corylaceae); exceptions were Artemisia, whose SPIn decreased over time (−26 p/m3 per 10yrs), and Ambrosia, Betula, and Urticaceae, which did not show a clear trend. For families, pollen seasons showed a trend for anticipation (from −4.7, Urticaceae, to −13.5 days/10yrs, Oleaceae) and extended duration (from 8.3, Cupressaceae, to 13.3 days/10yrs, Oleaceae). An evident heterogeneity across the climatic areas was seen for the SPIn, with the subcontinental western area exhibiting the highest loads and most prominent increases over time. In conclusion, increased levels and prolonged seasons for several pollen taxa observed in the Veneto Region, particularly in subcontinental areas, suggest increasing risks for individuals with pollen allergies. Addressing this issue requires measures of mitigation and adaptation to climate-driven changes in pollen exposure.

Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul

Rapid population growth in megacities such as Istanbul has led to various effects, such as industrialization, urbanization, loss of green areas, increasing vehicle traffic, and higher consumption of fossil fuels. These reasons, along with many other environmental factors, contribute to the rise of air pollution in urban life. This study aimed to examine the relationship between urbanization and air pollution in Istanbul. For this purpose, land cover maps covering Istanbul province were produced using Landsat-5 (TM), Landsat-8 (OLI), and Sentinel-2 (MSI) images for the years 1996 to 2021 at three-year intervals on the Google Earth Engine platform. Land cover for classification purposes was divided into five different classes: forest, water surface, urban area, and bare land, and classified using a random forest machine learning algorithm. To examine the impact of this urban area growth on air pollution, in the second step of the study, the column number density values of Sentinel 5P (TROPOMI) data for SO2, NO2, CO, and O3 gases for 2019, 2020, and 2021 were analyzed. The averages of the data from 39 air pollution monitoring stations across Istanbul were also examined. According to this classification, the urban area expanded from 491 km2 in 1996 to 1222 km2 by 2021. Considering the total surface area of Istanbul province, the urban area, which was 9% in 1996, reached 23% by 2021. The TROPOMI values were calculated as follows: the average column number density values for SO2, NO2, CO, and O3 were 0.0003538 mol/m², 0.0339514 mol/m², 0.0000984 mol/m², and 0.1453515 mol/m², respectively. Similarly, the gas concentrations of SO2, NO2, CO, and O3 measured from the ground stations were calculated as 6.603 µ/m3, 786,815 µ/m3, 43.763 µ/m3 and 45.773 µ/m3, respectively. Correlations between urbanization and TROPOMI values revealed a positive correlation of 0.39, 0.02, and 0.80 for SO2, NO2, and CO gases, while a negative correlation of 0.25 was found for O3 gas. The study also examined correlations between TROPOMI and ground station measurements, resulting in positive correlations of 0.55, 0.66, and 0.16 for SO2, NO2, and CO gases, respectively, while a negative correlation of 0.05 was found for O3 gas. Based on these findings, among the air pollutants studied both through TROPOMI and ground station data, the highest correlation was observed for CO gas.