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According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.
ABSTRACT
EditorsAgus Setiawan, Iis Triyulianti, Charlie Ester de Fretes, Muhammad Zain Tuakia, Sem Likumahua, Faisal Hamzah, Corry Yanti Manullang, Dewi Seswita Zilda, Abdul Wahab Radjab, Muhammad Fadli, Rafidha Dh. Ahmad Opier, Ahmad Romdon.PrefaceThe evaluation of the First Symposium on Banda Sea Ecosystem (ISBSE) held by the Research Center for Deep Sea (then under the Indonesian Institute of Sciences-LIPI) in 2017 indicated the need to expand the geographic coverage area of the symposium. As the follow up, in 2022, the Research Center organized the International Symposium on Eastern Indonesia Marine Ecosystems (ISEIME), with the objective is to gather all marine scientists and observers to meet and share their knowledge and recent information regarding marine ecosystems in eastern Indonesia and the country in general. ISEIME is one of various international conferences that organized by the Indonesian National Research and Innovation Agency-BRIN to focus on topics such as Oceanography and Climate Change;Marine Ecosystems, Biodiversity and Ecology;Marine Monitoring and Management;Marine Pollution;and Marine Geosciences. The event took place on the 24th of November 2022.Human health problems due to the pandemic of COVID-19 in Indonesia has decreased recently, yet in some parts of the country still show slight increase cases. To this end, we decided to conduct the ISEIME 2022 virtually using the zoom platform, which was remotely organized from Ambon, eastern Indonesia. The symposium was commenced by a report from the chairman of ISEIME and subsequently followed by a welcoming-remarks by the Head of Research Center for Deep Sea, Intan Suci Nurhati, Ph.D. In the first session, two keynote speakers (Prof. Ocky Karna Radjasa and Prof. Dwi Listyo Rahayu from BRIN) were given 30-40 minutes to present their talks and followed by a 30 minutes Q&A. Three invited speakers, Prof. Stevan Steinke, Prof. Wiedong Yu and Prof. Madya Dr. Tuan Nurul Sabiqah Tuan Anuar delivered their talks during the second session. In the parallel session, 30 speakers were divided into five different rooms according to the topics and they were given 15 minutes each to deliver their presentations, followed by 5 minutes Q&A.The event has gained a great success due to hard work from the collaboration between the local committee in Ambon, Bali and Jakarta. We would like to thank BRIN International Conference Event Organizer who had taken part in providing time and financial support during the event. Special acknowledgement to all speakers who contribute in the event by imparting their knowledge during the talks and discussions, and also their willingness to participate and contribute in the future marine research in Indonesia. We would also like to extend our sincere thanks to all authors who contribute their findings through their written papers, and for their significant thoughts and ideas in discussions during their presentations. Finally, we give a great appreciation and many thanks to reviewers who have voluntarily participated and contributed not only in judging papers, but also in providing constructive comments and suggestions for authors to improve their manuscripts.ISEIME chairmanSem LikumahuaList of ISEIME Committee is available in this Pdf.
ABSTRACT
The sustainability and progress of humanity depend on a clean, pollution-free environment, which is essential for good health and hygiene. Huge indoor auditorium does not have proper ventilation for air flow so when the auditorium is crowded the carbon di-oxide is emitted and it stays there for many days this may be a chance to spreading of COVID-19 and other infectious diseases. Without proper ventilation virus may present in the indoor auditorium. In the proposed system, emissions are detected by air, noise, and dust sensors. If the signal limit is exceeded, a warning is given to the authorities via an Android application and WiFi, and data is stored in cloud networks. In this active system, CO2 sensor, noise sensor, dust sensor, Microcontroller and an exhaust fan are used. This ESP-32 based system is developed in Arduino Integrated Development Environment (Aurdino IDE) to monitor air, dust and noise pollution in an indoor auditorium to prevent unwanted health problems related to noise and dust. More importantly, using IoT Android Application is developed in Embedded C, which continuously records the variation in levels of 3 parameters mentioned above in cloud and display in Android screen. Also, it sends an alert message to the users if the level of parameters exceeds the minimum and maximum threshold values with more accuracy and sensitivity. Accuracy and sensitivity of this products are noted which is very high for various input values. © 2022 IEEE.
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The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.
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Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.
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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.
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Air pollution severely compromises children's health and development, causing physical and mental implications. We have explored the use of site-specific green infrastructure (green barriers) in a school playground in Sheffield, UK, as an air-pollution-mitigation measure to improve children's environment. The study assessed air quality pre-post intervention and compared it with two control sites. Nitrogen dioxide (NO2) and particulate matter <2.5 µm in size (PM2.5) concentration change was assessed via three methods: (1) continuous monitoring with fixed devices (de-seasonalised);(2) monthly monitoring with diffusion tubes (spatial analysis);(3) intermittent monitoring with a mobile device at children's height (spatial analysis). De-seasonalised results indicate a reduction of 13% for NO2 and of 2% for PM2.5 in the school playground after two years of plant establishment. Further reductions in NO2 levels (25%) were observed during an exceptionally low mobility period (first COVID-19 lockdown);this is contrary to PM2.5 levels, which increased. Additionally, particles captured by a green barrier plant, Hedera helix ‘Woerner', were observed and analysed using SEM/EDX techniques. Particle elemental analysis suggested natural and potential anthropogenic origins, potentially signalling vehicle traffic. Overall, green barriers are a valid complementary tool to improve school air quality, with quantifiable and significant air pollution changes even in our space-constrained site. © 2023 by the authors. Licensee MDPI, Basel, Switzerland.
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Currently, in the modeling of various atmospheric pollutants, the simulation of independent trace gases (SO2 and O3) is constrained by the insufficient resolution of key remote sensing products, resulting in insufficient simulation reliability. In this study, spatial sampling and parameter convolution are combined to optimize LightGBM by utilizing ground observations, remote sensing products, meteorological data, assistance data, and random ID. Through the above techniques and an sequentialsimulation of air pollutants, we produce seamless daily 1-km-resolution products of PM2.5, SO2 and O3 for most parts of China from 2015 to 2020. Through random sampling, random site sampling, area-specific validation, comparisons of different models, and a cross26 sectional comparison of different studies, we verified that our simulations of the spatial distribution of multiple atmospheric pollutants are reliable and effective. The CV of the random sample yielded an R² of 0.88 and an RMSE of 9.91 ㎍/m³ for PM2.5, an R² of 0.89 and an RMSE of 4.62 ㎍/m³ for SO2, and an R² of 0.91 and an RMSE of 6.88 ㎍/m3 for O3. Combined with the SHapley Additive exPlanations (SHAP) approach, the roles of different parameters in the simulation process were clarified, and the positive role of parameter convolution was confirmed. Our dataset was used to assess the changes in the Air Pollution Index (API) in China before and after the outbreak of COVID-19, and the results indicate that these 34 changes were relatively small huge, suggesting that the epidemic control measures in 2020 were effective. The study demonstrates that the multipollutant datasets produced with the proposed models are of great value for long-term, large-scale, and regional-scale air pollution monitoring and prediction, as well as population health evaluation. [ABSTRACT FROM AUTHOR] Copyright of Earth System Science Data Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
As a pandemic, COVID 19 spread worldwide in early 2020. Primarily densely populated countries had remained vulnerable due to this biological hazard. Many people were forced to stay home owing to nature of the disease and no respite. A nationwide lockdown was implemented in India for 29 days (March 24th to April 21st) of 2020 during the wake of the COVID-19 pandemic. During the nationwide lockdown, industries, transport, and other commercial activities were suspended, except for necessary services. During the entire pandemic situation, an affirmative impact was observed as the air quality was reported to have improved worldwide. The complete economic lockdown to check COVID-19, brought unforeseen relief from severe condition of air quality. An apparent, reduction in level of PM2.5 and Air Quality Index (AQI) was experienced over Mumbai, Delhi, Kolkata, Hyderabad, and Chennai. Present work explores the various metrics of air pollution in Kolkata, West Bengal, India (imposed as a result of containment measure for COVID-19). The polluting parameters (e.g., PM10, PM2.5, SO2, NO2, CO, O3, and NH3) were chosen for seven monitoring stations (Ballygunge, Fort William, Victoria, Bidhannagar, Jadavpur, Rabindra Bharati, Rabindra Sarabar), which are spread across the metropolitan area of Kolkata. National Air Quality Index (NAQI) has been used to show pre-and during-lockdown air quality spatial patterns. The findings showed major changes in air quality throughout the lockdown period. The highest reduction in pollutants emission was observed for: PM10 (- 60.82%), PM2.5 (-45.05%) and NO2 (-62.27%), followed by NH3 (- 32.12%) and SO2 (-32.00%), CO (-47.46%), O3 (15.10%). During the lockdown, the NAQI value was reduced by 52.93% in the study area. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.
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The Bohai Bay as a typical semi-enclosed bay in northern China with poor water exchange capacity and significant coastal urbanization, is greatly influenced by land-based inputs and human activities. As a class of pseudo-persistent organic pollutants, the spatial and temporal distribution of Pharmaceuticals and Personal Care Products (PPCPs) is particularly important to the ecological environment, and it will be imperfect to assess the ecological risk of PPCPs for the lack of systematic investigation of their distribution in different season. 14 typical PPCPs were selected to analyze the spatial and temporal distribution in the Bohai Bay by combining online solid-phase extraction (SPE) and HPLC-MS/MS techniques in this study, and their ecological risks to aquatic organisms were assessed by risk quotients (RQs) and concentration addition (CA) model. It was found that PPCPs widely presented in the Bohai Bay with significant differences of spatial and seasonal distribution. The concentrations of ∑PPCPs were higher in autumn than in summer. The distribution of individual pollutants also showed significant seasonal differences. The high values were mainly distributed in estuaries and near-shore outfalls. Mariculture activities in the northern part of the Bohai Bay made a greater contribution to the input of PPCPs. Caffeine, florfenicol, enrofloxacin and norfloxacin were the main pollutants in the Bohai Bay, with detection frequencies exceeding 80 %. The ecological risk of PPCPs to algae was significantly higher than that to invertebrates and fish. CA model indicated that the potential mixture risk of total PPCPs was not negligible, with 34 % and 88 % of stations having mixture risk in summer and autumn, respectively. The temporary stagnation of productive life caused by Covid-19 weakened the input of PPCPs to the Bohai Bay, reducing the cumulative effects of the pollutants. This study was the first full-coverage investigation of PPCPs in the Bohai Bay for different seasons, providing an important basis for the ecological risk assessment and pollution prevention of PPCPs in the bay. © 2022 Elsevier B.V.
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The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd
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In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID‐19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21‐days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved.
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Malaysia is currently facing the COVID-19 pandemic which has claimed hundreds of innocent lives. Because of the health problems impose by the pandemic, the government has ordered to implement the movement control order (MCO) starting March 18, 2020. With this movement control order in place, people are not allowed to leave the house. Therefore, movement on the road can also be reduced. This study was conducted to investigate the emission concentrations of ground-level ozone into the air during MCOs in an urbanized area of Shah Alam. This secondary data of ground-level ozone (O3) was acquired from the Department of Environment in 2020 and interpreted using the methods of box and whisker plot, time series analysis, and diurnal variation plot. The results found that the concentrations of air pollutants in each type of MCOs implemented were different in terms of trends. During the early implementations of MCOs, results showed that there was a slight decrement in O3 concentrations and as MCOs periods continued, there higher decrements in O3 concentrations were observed. During the MCOs period, non-exceedance episodes were recorded which show the level of ground-level ozone was significantly improved result of the implementation of MCOs. Results also suggested there is a shift in the peak concentration time as the plot showed peak concentrations were reached between 4 to 6 p.m. which is quite later as normally peaks O3 concentrations were reported normally reached from 12 noon to 2 p.m. [13]
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Resilient and sustainable infrastructure development is necessary to support innovative industries. Batang Regency is one of the regencies on the island of Java that is currently intensively building infrastructure to prepare the Batang Integrated Industrial Estate (KITB). Therefore, the government also supports this Presidential Regulation Number 79 of 2019 and Presidential Regulation No. 109 of 2020, which observes the development of the Batang Regency Integrated Industrial Estate. When the Covid-19 pandemic hit Indonesia in early March 2020, many changes occurred in the infrastructure development process. Some infrastructure has been temporarily suspended due to the Covid-19 pandemic. Of course, this will be followed by a decrease in emissions due to limited movement and infrastructure development there. This study wants to analyze how the air changes from the beginning of the pandemic until 2022. The air changes will be seen by monitoring NO2 formed from emissions from cars, trucks, buses, and industry. This is intended to measure/identify how the pattern of air changes considering the Batang District is passed by the Pantura road so that there is a high intensity of movement. The method used is spatial analysis with google earth engine Sentinel 5P images. The result of this study can provide input monitoring emissions related to technological advances in the era of open data.
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We use satellite methane observations from the Tropospheric Monitoring Instrument (TROPOMI), for May 2018 to February 2020, to quantify methane emissions from individual oil and natural gas (O/G) basins in the US and Canada using a high-resolution (∼25 km) atmospheric inverse analysis. Our satellite-derived emission estimates show good consistency with in situ field measurements (R=0.96) in 14 O/G basins distributed across the US and Canada. Aggregating our results to the national scale, we obtain O/G-related methane emission estimates of12.6±2.1 Tg a-1 for the US and 2.2±0.6 Tg a-1 for Canada, 80 % and 40 %, respectively, higher than the national inventories reported to the United Nations. About 70 % of the discrepancy in the US Environmental Protection Agency (EPA) inventory can be attributed to five O/G basins, the Permian, Haynesville, Anadarko, Eagle Ford, and Barnett basins, which in total account for 40 % of US emissions. We show more generally that our TROPOMI inversion framework can quantify methane emissions exceeding 0.2–0.5 Tg a-1 from individual O/G basins, thus providing an effective tool for monitoring methane emissions from large O/G basins globally.
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The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightningNOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (-13 %) and Houston (-20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishingNO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with -9 % underestimate in eastern Texas and -21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.
ABSTRACT
Airport emissions have received increased attention because of their impact on atmospheric chemical processes, the microphysical properties of aerosols, and human health. At present, the assessment methods for airport pollution emission mainly involve the use of the aircraft emission database established by the International Civil Aviation Organization, but the emission behavior of an engine installed on an aircraft may differ from that of an engine operated in a testbed. In this study, we describe the development of a long-path differential optical absorption spectroscopy (LP-DOAS) instrument for measuring aircraft emissions at an airport. From 15 October to 23 October 2019, a measurement campaign using the LP-DOAS instrument was conducted at Hefei Xinqiao International Airport to investigate the regional concentrations of various trace gases in the airport’s northern area and the variation characteristics of the gas concentrations during an aircraft’s taxiing and take-off phases. The measured light path of the LP-DOAS passed through the aircraft taxiway and the take-off runway concurrently. The aircraft’s take-off produced the maximum peak in NO2 average concentrations of approximately 25 ppbV and SO2 average concentrations of approximately 8 ppbV in measured area. Owing to the airport’s open space, the pollution concentrations decreased rapidly, the overall levels of NO2 and SO2 concentrations in the airport area were very low, and the maximum hourly average NO2 and SO2 concentrations during the observation period were better than the Class 1 ambient air quality standards in China. Additionally, we discovered that the NO2 and SO2 emissions from the Boeing 737–800 aircraft monitored in this experiment were weakly and positively related to the age of the aircraft. This measurement established the security, feasibility, fast and non-contact of the developed LP-DOAS instrument for monitoring airport regional concentrations as well as NO2 and SO2 aircraft emissions during routine airport operations without interfering with the normal operation of the airport.
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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.
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Air pollution causes several diseases like suffocation, chronic obstructive pulmonary disease (COPD), lung cancer, throat infection, and so forth. So, there is a need to monitor indoor air quality for the safety of human life. Indoor air pollution is even more dangerous than outdoor air pollution. Even, after the COVID-19 pandemic, humans are spending most of their time in indoor houses. In addition to this, air pollution is increasing day by day due to varying climate changes. In view of this fact, this research wor has designed and developed a novel system based on the latest IoT technology that monitors indoor air quality and provides a web portal for data visualization. The proposed system consists of several gas sensors integrated on a single PCB that helps in reading seven pollutants like CO2, CO, O3, NO2, VOC, and Particulate Matter along with humidity and temperature. In our work, Raspberry Pi acts as a processor as well as the communicating node to the cloud. The experimental setup is deployed in several indoor places like closed labs, classrooms, homes, etc., where humans spend more time. Raspberry Pi is having an inbuilt wi-fi functionality and the real-time data is sent to Google Firebase with help of a Jio Fi router. After visualizing the data, Indoor Air Quality Index (IAQI) is measured and generates an alarm for the safety of humans when air standard crosses a marginal value. © 2022 IEEE.
ABSTRACT
Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.