Areas within the United States at the Highest Risk for African Swine Fever, Classical Swine Fever, and Foot-and-Mouth Disease Introduction

Domestic livestock production is a major component of the agricultural sector, contributing to food security and human health and nutrition and serving as the economic livelihood for millions worldwide. The impact of disease on global systems and processes cannot be understated, as illustrated by the effects of the COVID-19 global pandemic through economic and social system shocks and food system disruptions. This study outlines a method to identify the most likely sites of introduction into the United States for three of the most concerning foreign animal diseases: African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). We first created an index measuring the amount of potentially contaminated meat products entering the regions of interest using the most recently available Agricultural Quarantine Inspection Monitoring (AQIM) air passenger inspection dataset, the AQIM USPS/foreign mail, and the targeted USPS/foreign mail interception datasets. The risk of introduction of a given virus was then estimated using this index, as well as the density of operations of the livestock species and the likelihood of infected material contaminating the local herds. Using the most recently available version of the datasets, the most likely places of introduction for ASF and CSF were identified to be in central Florida, while FMD was estimated to have been most likely introduced to swine in western California and to cattle in northeastern Texas. The method illustrated in this study is important as it may provide insights on risk and can be used to guide surveillance activities and optimize the use of limited resources to combat the establishment of these diseases in the U.S.

Monitoring ambient air pollution and pulmonary function in asthmatic children by mobile applications in COVID-19 pandemic.

BACKGROUND: Several public health measures were implemented during the COVID-19 pandemic. However, little is known about the real-time assessment of environmental exposure on the pulmonary function of asthmatic children. Therefore, we developed a mobile phone application for capturing real-time day-to-day dynamic changes in ambient air pollution during the pandemic. We aim to explore the change in ambient air pollutants between pre-lockdown, lockdowns, and lockdowns and analyze the association between pollutants and PEF mediated by mite sensitization and seasonal change. METHOD: A prospective cohort study was conducted among 511 asthmatic children from January 2016 to February 2022. Smartphone-app used to record daily ambient air pollution, particulate matter (PM2.5, PM10) Ozon (O3), nitrogen dioxide (NO2), Carbon Monoxide (CO), sulfur dioxide (SO2), average temperature, and relative humidity, which measured and connected from 77 nearby air monitoring stations by linking to Global Positioning System (GPS)-based software. The outcome of pollutants' effect on peak expiratory flow meter (PEF) and asthma is measured by a smart peak flow meter from each patient or caregiver's phone for real-time assessment. RESULTS: The lockdown (May 19th, 2021, to July 27th, 2021) was associated with decreased levels of all ambient air pollutants aside from SO2 after adjusting for 2021. NO2 and SO2 were constantly associated with decreased levels of PEF across lag 0 (same day when the PEF was measured), lag 1 (one day before PEF was measured), and lag 2 (two days prior when the PEF was measured. Concentrations of CO were associated with PEF only in children who were sensitized to mites in lag 0, lag 1, and lag 2 in the stratification analysis for a single air pollutant model. Based on the season, spring has a higher association with the decrease of PEF in all pollutant exposure than other seasons. CONCLUSION: Using our developed smartphone apps, we identified that NO2, CO, and PM10 were higher at the pre-and post-COVID-19 lockdowns than during the lockdown. Our smartphone apps may help collect personal air pollution data and lung function, especially for asthmatic patients, and may guide protection against asthma attacks. It provides a new model for individualized care in the COVID era and beyond.

Recent Urban Issues Related to Particulate Matter in Ploiesti City, Romania

The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.

Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years' worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models' performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Effect of COVID-19 pandemic restrictions on air pollution at a local scale in urban areas affected by high-intensity vehicle traffic in Poland

The lockdown in 2020 implemented due to the SARS-CoV-2 pandemic has resulted in a significant improvement in air quality at a global scale. Nationwide lockdown also considerably improved air quality at a local scale, especially in cities which were almost completely shut down during the first coronavirus wave, with nearly no activity. We tested the hypothesis that a reduction in the intensity of vehicle traffic causes a drastic decrease in urban air pollution at a local scale. We focused on two urban agglomerations, Warsaw and Cracow, in Poland. Data of the concentrations of traffic-related sources, namely NOx, PM10, and PM2.5, obtained from two air pollution monitoring stations were analyzed for the years 2020 and 2021, during which lockdown and pandemic restrictions were in effect, and for 2019, as a reference. In the years 2020–2021, the average annual concentration of NOx was decreased by ~ 19%, PM2.5 by ~ 19%, and PM10 by ~ 18% in Warsaw, while in Cracow the average annual concentration of NOx was decreased by ~ 16%, PM2.5 by ~ 22%, and PM10 by ~ 2%, compared to 2019. The contribution from traffic-related sources to the overall level of air pollution was estimated. The results indicated that ~ 30 µg/m3 of PM10, ~ 15 µg/m3 of PM2.5, and ~ 120 µg/m3 of NOx in Cracow, and ~ 20 µg/m3 of PM2.5 in Warsaw originate from moving vehicles. The nationwide lockdown allowed us to conduct this study to understand how a reduction in local traffic emissions can decrease ambient air pollution levels.

Sustainable policies for air pollution reduction after COVID‐19 pandemic: Lessons learnt from the impact of the different lockdown periods on air quality

Due to the COVID‐19 pandemic, governments imposed several mobility restrictions which can be used to evaluate their impact on air quality and generate better‐targeted policies to improve it. Therefore, this study aimed to define sustainable mitigation measures to reduce air pollution based on quantifying the impacts of the restrictions imposed during the COVID‐19 pandemic on air quality in Portugal. Thus, hourly concentrations of PM10, PM2.5, NO2, O3, CO and SO2 were obtained from the Portuguese Air Quality Monitoring Network. Data was then divided into six periods (2020–2021) and compared with the corresponding historical periods (2015–2019). Furthermore, the satellite data of NO2, CO, and absorbing aerosol index (AAI) from the sentinel‐5P TROPOMI was collected to complement the analysis conducted for the monitoring data. Overall, air quality improved in all study periods and areas, except in industrial sites. The satellite data corroborated the results herein achieved and thus validated the real effect of the measures adopted in the country during the pandemic on air quality. Sustainable policies to improve air quality could include remote (or hybrid) work whenever possible as a long‐term measure and prohibition of travelling between municipalities when an extraordinary event of high air pollution is predicted or occurs. Other policies should be adopted for industrial areas. Given this, and as the restrictive mobility measures had a strong effect on reducing air pollution, the post‐COVID era represents an opportunity for society to rethink future mobility and other emerging policies, that should favour softer and cleaner means of transportation.

Evaluation Of Indoor Air Quality In Hospitals: With Respect To Green Building Rating Systems

The World Health Organization tightened the various indoor air quality parameters to improve the quality of the air globally during COVID-19. In light of this, research (Pollution and Health: a progress update, 2022)made public in India in 2022 shows that air pollution is a major problem worldwide, with a projected 66.7 lac people dying as a result. Similar issues are present in developing countries like India, where 16.7 million fatalities were expected in 2019. According to the study (Air Quality Life Index: India Fact Sheet, 2022), air pollution shortens Indians' life expectancy by an average of 5 years. It is crucial to check that these new standards after Covid are compatible with the Green Building Rating Systems. The study is also based on in-depth discussions with doctors, administrators, green building designers, and building tenants, as well as surveys using questionnaires and interviews. The existing Green Rating Systems were found to require revision, with the weighting of the elements linked to air quality requiring strengthening with the installation of appropriate air quality monitoring of different contaminants. The study's main objective is to examine the air quality parameters, their weightings, and the monitoring tools. The study was focused on the evaluation of Green Rated healthcare buildings in India based on different Air Quality parameters. The overall evaluation of the air quality is found to be very critical and, in the areas, where extreme care and precautions are required with respect to cleanliness and hygiene like the Intensive care unit and Operation Theatre the values of the air pollutants like Formaldehyde, Volatile Organic Compound, Carbon Monoxide and Carbon Di Oxide are indicating serious problems. Copyright © 2022 Ubiquity Press. All rights reserved.

A low-cost air quality monitoring system based on Internet of Things for smart homes

Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems;It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT+CRF Model

(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for air pollution control by combining regional air monitoring data. (2) Methods: By selecting the air pollution complaint information in Beijing from 2019 to 2020, in this study, we extract the names and addresses of complaint points, as well as the complaint times and types by adopting the BERT (bidirectional encoder representations from transformers) + CRF (conditional random field) model deep learning method. Moreover, through further filtering and processing of the complaint points’ address information, we achieve address matching and spatial positioning of the complaint points, and realize the regional spatial representation of air pollution complaints in Beijing in the form of a heat map. (3) Results: The experimental results are compared and analyzed with the ranking data of total suspended particulate (TSP) concentration of townships (streets) in Beijing during the same period, indicating that the key areas of air pollution complaints have a high correlation with the key polluted township (street) areas. The distribution of complaints and the types of complaints in each township (street) differ according to the population density in each township (street), the level of education, and economic activity. (4) Conclusions: The results of this study show that the public, as the intuitive perceiver of air pollution, is sensitive to the air pollution situation at a smaller spatial scale;furthermore, complaints can provide guidance and reference for the direction of air pollution control and law enforcement investigations when coupled with geographical features and economic status.

SARS-CoV-2 Surveillance in Indoor Air Using Electrochemical Sensor for Continuous Monitoring and Real-Time Alerts.

The severe acute respiratory syndrome related coronavirus 2 (SARS-CoV-2) has spread globally and there is still a lack of rapid detection techniques for SARS-CoV-2 surveillance in indoor air. In this work, two test rigs were developed that enable continuous air monitoring for the detection of SARS-CoV-2 by sample collection and testing. The collected samples from simulated SARS-CoV-2 contaminated air were analyzed using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The test rigs utilized two air sampling methods: cyclone-based collection and internal impaction. The former achieved a limit of detection (LoD) of 0.004 cp/L in the air (which translates to 0.5 cp/mL when tested in aqueous solution), lower than the latter with a limit of 0.029 cp/L in the air. The LoD of 0.5 cp/mL using the UFC-19 sensor in aqueous solution is significantly lower than the best-in-class assays (100 cp/mL) and FDA EUA RT-PCR test (6250 cp/mL). In addition, the developed test rig provides an ultra-fast method to detect airborne SARS-CoV-2. The required time to test 250 L air is less than 5 min. While most of the time is consumed by the air collection process, the sensing is completed in less than 2 s using the UFC-19 sensor. This method is much faster than both the rapid antigen (<20 min) and RT-PCR test (<90 min).

A Carbon Nanotube-Functional Polymer Composite Film for Low-Power Indoor CO₂ Monitoring

Indoor air quality (IAQ) has been a growing concern in recent years, only to be expedited by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is commonly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. This work presents a polymer composite-based chemiresistive CO2 sensor that leverages branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG) as the CO2 absorbing layer. This polymer blend was incorporated with single wall carbon nanotubes (CNT), which serve as the charge carriers. Prototype sensors were assessed in a bench-top environmental test chamber which varied temperature (22–26 °C), relative humidity level (20–80%), CO2 concentration (400–20,000 ppm), as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as a low-power alternative to current commercially available technologies for indoor CO2 monitoring.

A Simple and Low-cost Photocatalytic Air Purification Test Method

The application of photocatalytic oxidation (PCO) in air purifiers (AP) to remove viruses, bacteria, and toxic gases in the air is intensively being studied, especially after the Covid-19 pandemic broke out. The testing method of PCO materials for AP purposes has been standardized through ISO 22197-4 (2013). However, the standard required a set of complex, high precision, and costly equipment. The present study demonstrates a simpler and low-cost test setup without compromising any accuracy in the overall result. The proposed test consists of a test chamber and mixing chamber, and sets of equipment installed in it. A 3D printer fabricated a PCO reactor, and TiO2 was coated on the surface. Formaldehyde (HCHO) is used as a sample pollutant to be observed, injected into the test chamber. Before the measurement of the concentration of HCHO, the intensity of UV A LED was measured. Then, the amount of formaldehyde concentration was monitored online by indoor air quality measurement equipment. The result shows that the intensity of UV light was enough to generate a photocatalytic oxidation reaction. After 20 minutes of reaction, the HCHO concentration inside the chamber was decreased around 21.76%.

Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution

Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground and to support attribution of sources. We highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to reduce the uncertainties and to advance diagnostic understanding and prediction of air pollution. There are three reasons for the implementation of stereoscopic monitoring: 1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions;2) satellite retrievals of air pollutants are widely used in air pollution studies, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles;and 3) air quality modeling and forecasting require 3D observational constraints. We call on researchers and policymakers to establish stereoscopic monitoring networks and share monitoring data to better characterize the formation of air pollution, optimize air quality management, and protect human health. Future directions for advancing monitoring and modeling/predicting air pollution are also discussed.

Use of Low-Cost Sensors to Characterize Occupational Exposure to PM2.5 Concentrations Inside an Industrial Facility in Santa Ana, CA: Results from a Worker- and Community-Led Pilot Study

PM2.5 is an air contaminant that has been widely associated with adverse respiratory and cardiovascular health, leading to increased hospital admissions and mortality. Following concerns reported by workers at an industrial facility located in Santa Ana, California, workers and community leaders collaborated with experts in the development of an air monitoring pilot study to measure PM2.5 concentrations to which employees and local residents are exposed during factory operating hours. To detect PM2.5, participants wore government-validated AtmoTube Pro personal air monitoring devices during three separate workdays (5 AM–1:30 PM) in August 2021. Results demonstrated a mean PM2.5 level inside the facility of 112.3 µg/m3, nearly seven-times greater than outdoors (17.3 µg/m3). Of the eight workers who wore personal indoor sampling devices, five showed measurements over 100 μg/m3. Welding-related activity inside the facility resulted in the greatest PM2.5 concentrations. This study demonstrates the utility of using low-cost air quality sensors combined with employee knowledge and participation for the investigation of workplace air pollution exposure as well as facilitation of greater health-related awareness, education, and empowerment among workers and community members. Results also underscore the need for basic measures of indoor air pollution control paired with ongoing air monitoring within the Santa Ana facility, and the importance of future air monitoring studies aimed at industrial facilities.

Air Quality Status in Konya City Centre, Konya, Turkey during Pandemic Covid-19

High developed industry regions, provincial centers with a heavy traffic and dense populations with cold winters, using low-quality fossil fuel consumption have an effect on quality of life especially for people with respiratory diseases. The air quality data gathered from air monitoring stations for the City Center of Konya, Turkey were analysed statistically during the period when curfew due to the Covid-19 pandemic in 2020. The restrictions that cause the reduction of vehicle exhaust emissions, which are important factors in the formation of some air pollutants, are thought to be effective in improving the air quality as well as meteorological conditions are effective on the days when the air quality is analysed. It is thought that the reduction in HC, NOx, CO and PM emissions, which can be evaluated as exhaust emissions, will be an effective factor due to the restrictions of Covid-19 pandemic. Comparing between the 2019 and 2020 MCO data, we identified that most of the gases decreased with NO2 (-24 µg/m3), SO2 (-24 µg/m3), CO (-37 µg/m3) and an increment of O3 of +50 µg/m3 which indicates that the MCO and restricted of movement were give an impact to air quality levels in Konya City. The increase in O3 values were found by the existing of the sun rays in the atmosphere with the formation of O3 during the clean air period.

Re-evaluation of hazard classifications and exposure limits for lead – An example for an advanced mode of operation within the MAK commission during COVID-19 restrictions

Introduction: The Permanent Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area of the Deutsche Forschungsgemeinschaft (MAK Commission) was founded in 1955 to evaluate hazards and exposure limits (MAK values) and to counsel the German authorities of occupational health and safety prevention. Since then, tasks, evaluation concepts, communication processes and operating structure advanced continuously. Material and Methods: Today, the structure of the commission consists of four core groups (“MAK”, “BAT”, “Air monitoring”, “Biomonitoring”), accompanied by four working groups for the assessment of specific compound groups, four working groups for specific classifications, three working groups for adverse outcome pathways and epidemiology, as well as ad-hoc working groups for current issues. The mode of operation and interworking between the working groups and the changeover to online communication processes is demonstrated using the recent evaluation of lead. Results: In the period September 2020 - February 2021 the MAK commission performed a complete re-evaluation of lead. Coordinated by the core groups “MAK” and “BAT”, accompanied by the subgroups “Lead”, “Carcinogenesis”, “Developmental toxicology” and “Neurotoxicity”, the MAK Commission classified lead and its inorganic compounds as category 4 carcinogens and in pregnancy risk group A and set new BAT and MAK values. Conclusion: The extremely efficient workflow of the reevaluation of hazard classifications and exposure limits was crucially supported by the new online communication processes which was pushed by the COVID-19 restrictions.

"AIR PROTECTION 2021" 12th CROATIAN SCIENTIFIC AND PROFESSIONAL CONFERENCE with international participation 15-17 September 2021, Medulin, Croatia

The participants of "Air Protection 2021" presented their experiences and the issues they encounter in their work through the 54 presentations in following topics: * Topic 1 - Managing air quality - inspection and control * Topic 2 - Atmospheric emissions of pollutants * Topic 3 - Monitoring ambient air pollution * Topic 4 - Developing and testing measuring methods * Topic 5 - Estimating exposure to air pollutants and impact on health * Topic 6 - Air Protection in physical planning, construction, and environmental protection * Topic 7 - EFCA session "Air pollution from shipping emissions" * Topic 8 - IUAPPA and Global Forum special session: "Respiratory pandemics and air pollution: exploring the links" The conference started on 15th September with three introductory lectures: * Richard Mills (IUAPPA): GLOBAL AIR POLLUTION: LESSONS FROM THE LAST 20 YEARS * Sandra Krmpotić, Nina Zovko, Gordan Došen (Ministry of Economy and Sustainable Development of the Republic of Croatia): AN OVERVIEW OF LEGISLATION IN THE FIELD OF AIR QUALITY AT NATIONAL AND EU LEVEL * Vedranka Bobić (Expert witness for environmental protection, occupational safety and environmental accidents): EXPERT WITNESS EXAMINATION AND COURT PRACTICE OF ENVIRONMENTAL FACTORS NOT REGULATED BY LAW- ODOURS Introductory lectures were followed by 17 presentations in Croatian on Topic 1, 2, and 3. Due to her effort CAPPA was first admitted to IUAPPA and then in 1998 to EFCA, where Vladimira Vaðić held its vice-presidency since 2020 until her retirement. Since 2019 she is a honorary member of CAPPA. [...]Andrzej Jagusiewicz held a presentation on the cost and environmental benefits of IMO regulations of shiporiginated SOx and NOx emissions, assessed for the case of the Baltic sea.

Ambient BTEX Concentrations during the COVID-19 Lockdown in a Peri-Urban Environment (Orléans, France)

During the period from 17 March to 10 May 2020, France saw dramatic shifts in domestic, industrial and transport activities as a national lockdown was introduced. So far, studies have generally focused on urban settings, by contrast, this work reports data for a peri-urban location. Air samples were collected and analyzed using a fully automated GC-MS-FID system in an air quality monitoring station situated in the suburbs of Orléans, France. Average concentrations of BTEX (benzene, toluene, ethylbenzene, and xylenes) before, during, and after lockdown, were 402 ± 143, 800 ± 378 and 851 ± 445 pptv, respectively. Diurnal variation in BTEX and correlations between each of its components were analyzed to determine its various sources. The toluene/benzene (T/B) and m,p-xylene/ethylbenzene (MP/E) ratios, photochemical ages were used to explore whether the BTEX were from local or more distant sources. Together with a host of complementary measurements including NOx, O3, black carbon, meteorological parameters, and anthropogenic activities, we were able to make some inferences on the sources of BTEX. The results suggest that although anomalous local anthropogenic activity can lead to significant changes in BTEX concentrations, pollution levels in Orléans are mostly dependent on meteorological conditions, specifically whether the winds are coming from the Paris region. It appears, based on these measurements, that the pollution in the Orléans area is very much tied to the nearby megacity of Paris, this may be true for other peri-urban sites with implications for city planning and pollution mitigation strategies.

Air Quality Measurements in Kitchener, Ontario, Canada Using Multisensor Mini Monitoring Stations

The Region of Waterloo is the third fastest growing region in Southern Ontario in Canada with a population of 619,000 as of 2019. However, only one air quality monitoring station, located in a city park in Kitchener, Ontario, is currently being used to assess the air quality of the region. In September 2020, a network of AQMesh Multisensor Mini Monitoring Stations (pods) were installed near elementary schools in Kitchener located near different types of emission source. Data analysis using a custom-made long-distance scaling software showed that the levels of nitrogen oxides (NO and NO2), ground level ozone (O3), and fine particulate matter (PM2.5) were traffic related. These pollutants were used to calculate the Air Quality Health Index-Plus (AQHI+) at each location, highlighting the inability of the provincial air quality monitoring station to detect hotspot areas in the city. The case study presented here quantified the impact of the 2021 summer wildfires on the local air quality at a high time resolution (15-min). The findings in this article show that these multisensor pods are a viable alternative to expensive research-grade equipment. The results highlight the need for networks of local scale air quality measurements, particularly in fast-growing cities in Canada.