Pollution Measurements Research Articles

Particulate and gaseous air pollutants exceed WHO guideline values and have the potential to damage human health in Faisalabad, Metropolitan, Pakistan

First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019–2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g−1) and summer (1.17 mg g−1) and lower in autumn (55 mg g−1) and winter (1.08 mg g−1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.Graphical abstract

How do income inequality, poverty and industry 4.0 affect environmental pollution in South Asia: New insights from quantile regression

While many factors have been studied as potential causes of environmental degradation, the impact of poverty and inequality has been largely overlooked in the research. The Sustainable Development Goals are aligned with the intersection of poverty, inequality, and the environment. In addition, most previous research has used carbon dioxide (CO2) emissions as a surrogate for pollution. These gaps are filled by this study, which uses ecological footprint (a comprehensive measure of pollution) and CO2 emissions to examine the effects of income disparity and poverty on environmental pollution in 13 nations. Dynamic panel Quantile regression methods are used in this study because of their resilience to various econometric problems that can crop up during the estimate process. The empirical results reveal that the whole panel's carbon emissions and ecological footprint rise when income disparity and poverty exist. When the panel is subdivided, however, we see that income inequality reduces carbon emissions and environmental footprint for the wealthy but has the opposite effect on the middle class. While high-income households see no impact from poverty on their carbon emissions, middle-income households see an increase in both. Overall, the results of this study suggest that income disparity and poverty are major factors in ecological degradation. Therefore, initiatives to reduce environmental degradation should pay sufficient attention to poverty and inequality to achieve ecological sustainability.

Investigation of the chemical mechanism of pollutant formation in co-firing of ammonia and biomass lignin

Ammonia (NH3) and biomass are recognised as renewable energy carriers with substantial promise for reducing carbon emissions. However, the issue of nitrogen oxides (NOX, including NO and NO2) and nitrous oxide (N2O) emissions from ammonia combustion and soot from biomass combustion, remains a pressing concern due to their detrimental impacts on the environment and human health. This study investigated the mechanism of pollutant formation during ammonia-lignin co-firing through reactive molecular dynamics (RMD) simulations. The results indicate that co-firing a certain amount of ammonia with lignin can reduce the formation of soot. This is mainly due to NH2 radicals converting a portion of polycyclic aromatic hydrocarbon (PAH) precursors into C–N compounds. However, it is necessary to maintain higher ammonia concentrations. Adding a small quantity of ammonia would decrease the extent of oxidative reactions within the system and fail to generate enough NH2 radicals to consume the carbon atoms forming PAH precursors, ultimately resulting in increased soot production. The impact of lignin on the formation of NOX and N2O during ammonia combustion has two main aspects: Firstly, the oxidising radicals formed during the initial decomposition of lignin promote the generation of N–O bonds, leading to an increase in the quantities of NOX and N2O during the initial reaction stage. Secondly, the carbon-containing products produced from lignin decomposition consume nitrogen atoms by forming N–C bonds, thereby diminishing available nitrogen atoms for nitrogen oxides formation. This study provides new insights into the formation of primary pollutants of ammonia and biomass co-firing on an atomic scale, which can help develop pollutant mitigation measures.

Synergetic effect evaluation of pollution and carbon emissions in an industrial park: An environmental impact perspective

IntroductionHow to achieve synergistic control of pollution and carbon emissions has become a global concern. This article focuses on an industrial park and aims to evaluate the synergistic effect of pollution and carbon emissions from an environmental impact perspective. We propose a synergistic effect evaluation method based on life cycle assessment (LCA-SE method). The synergistic effect index (Els) indicates that the industrial park exhibits a strong synergistic effect in reducing pollution and carbon emissions. Based on our findings, we draw the following conclusions: First, carbon emissions should be given priority in emissions control. Second, photovoltaic power generation and energy conservation measures can effectively achieve synergistic efficiency in reducing pollution and carbon emissions. Third, when considering synergistic control of pollution and carbon emissions, priority should be given to pollutant reduction measures that can simultaneously reduce energy consumption during the process. This study presents a novel approach, the LCA-SE method, to assessing the synergistic impact of pollution and carbon emissions, offering valuable insights into the coordinated management of pollution and carbon emissions in China.

Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants

Abstract. Large uncertainties persist within current biomass burning (BB) inventories, and the choice of these inventories can substantially impact model results when assessing the influence of BB aerosols on weather and climate. We evaluated discrepancies among BB emission inventories by comparing carbon monoxide (CO) and organic carbon (OC) emissions from seven major BB regions globally between 2013 and 2016. Mainstream bottom-up inventories, including the Fire INventory from NCAR 1.5 (FINN1.5) and Global Fire Emissions Database version 4s (GFED4s), along with the top-down inventories Quick Fire Emissions Dataset 2.5 (QFED2.5) and Visible Infrared Imaging Radiometer (VIIRS-)based Fire Emission Inventory version 0 (VFEI0), were selected for this study. Global CO emissions range from 252 to 336 Tg, with regional disparities reaching up to a 6-fold difference. Dry matter is the primary contributor to the regional variation in CO emissions (50 %–80 %), with emission factors accounting for the remaining 20 %–50 %. Uncertainties in dry matter often arise from biases in calculating bottom fuel consumption and burned area, influenced by vegetation classification methods and fire detection products. In the tropics, peatlands contribute more fuel loads and higher emission factors than grasslands. At high latitudes, increased cloud fraction amplifies the discrepancy in estimated burned area (or fire radiative power) by 20 %. The global OC emissions range from 14.9 to 42.9 Tg, exhibiting higher variability than CO emissions due to the corrected emission factors in QFED2.5, with regional disparities reaching a factor of 8.7. Additionally, we applied these BB emission inventories to the Community Atmosphere Model version 6 (CAM6) and assessed the model performance against observations. Our results suggest that the simulations based on the GFED4s agree best with the Measurements of Pollution in the Troposphere (MOPITT-)retrieved CO. While comparing the simulation with the Moderate Resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD), our results reveal that there is no global optimal choice for BB inventories. In the high latitudes of the Northern Hemisphere, using GFED4s and QFED2.5 can better capture the AOD magnitude and diurnal variation. In equatorial Asia, GFED4s outperforms other models in representing day-to-day changes, particularly during intense burning. In Southeast Asia, we recommend using the OC emission magnitude from FINN1.5 combined with daily variability from QFED2.5. In the Southern Hemisphere, the latest VFEI0 has performed relatively well. This study has implications for reducing the uncertainties in emissions and improving BB emission inventories in further studies.

Ozone Pollution in the North China Plain during the 2016 Air Chemistry Research in Asia (ARIAs) Campaign: Observations and a Modeling Study

To study air pollution in the North China Plain (NCP), the Air Chemistry Research in Asia (ARIAs) campaign conducted airborne measurements of air pollutants in spring 2016. High pollutant concentrations, with O3 > 100 ppbv, CO > 500 ppbv, and NO2 > 10 ppbv, were observed. CMAQ simulations with the 2010 EDGAR emissions capture the spatial and temporal variations in ozone and its major precursors such as NO2 and VOCs, with significant underestimation. Differences between CMAQ simulations and satellite observations reflect changes in anthropogenic emissions, decreased NOx emissions in megacities such as Beijing, but slight increases in other cities and rural areas. CMAQ also underestimates HCHO and CO, suggesting adjustments of the 2010 EDGAR emissions are necessary. HCHO/NO2 column ratios derived from OMI measurements and CMAQ simulations show that VOC-sensitive chemistry dominates the ozone photochemical production in eastern China, suggesting the importance of tightening regulations on anthropogenic VOC emissions. After adjusting emissions based on satellite observations, better model performance was achieved. Because of the VOC-sensitive environment in ozone chemistry over the NCP, the underestimation of anthropogenic emissions could be important for CMAQ simulations, while future study and regulations should focus on VOC emissions with continuous controls on NOx emissions in China.

Enhanced groundwater vulnerability assessment to nitrate contamination in Chongqing, Southwest China: Integrating novel explainable machine learning algorithms with DRASTIC-LU

ABSTRACT Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. This study applies the DRASTIC-LU framework, nitrate distribution data, and three machine learning models (RF, XGB, SVM) to classify nitrate levels (exceeding 10 mg/L as nitrogen) in Chongqing, China. Model evaluation uses accuracy and F1 score metrics, with RF achieving the highest accuracy (92.9%), kappa (0.857), and AUC (0.948) on test dataset. Furthermore, the SHAP interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt of using machine learning models for groundwater vulnerability assessment in the Chongqing region. It provides theoretical support for the construction layout of groundwater monitoring stations and the prevention and control of groundwater pollution in the future.

Ars Genotype of Arsenic Oxidizing Bacteria and Detoxification

Objectives:The objectives of this study is bioremediation and detoxification of arsenite using arsenic resistance system (ars) genotypes of Arsenic Oxidizing Bacteria (AOB) isolated from highly As-contaminated mine.Methods:Bacterial strains that are resistant to arsenic were isolated from the Samkwang mine. The identification of AOB was conducted by analyzing the 16S rRNA gene using universal primers. To determine the genotypes of the arsenic resistance system (ars), specific primers were used for each gene. The extent of arsenic resistance was measured, and the efficiency of arsenite oxidation was assessed through a batch test. Arsenic concentration was measured using ICP-MS.Results and Discussion:The arsenic concentrations at site 1 of the Samkwang mine were found to be 1,322 mg/kg. This concentration is 26.4 times higher than the standard for soil pollution concerns (50 mg/kg) and 8.8 times higher than the standard for soil pollution measures (150 mg/kg). The appropriate remediation is studied such as bacterial remediation. The three efficient AOBs were identified as Agrobacterium tumefaciens EBC-SK1 (MF928870), Ochrobactrum anthrophi EBC-SK4 (MF928873), Ochrobactrum anthrophi EBC-SK12 (MF928881), respectively. The arsenic resistance system (ars) genotype were detected, which is the leader genes of the arsenic oxidation system (arsR and arsD), and the membrane gene (arsB). The arsB is involved in the encoding of the efflux/influx pumping system and moves arsenite into the bacterial cells. Arsenite-oxidizing (aox) genes are activated to oxidize arsenite into arsenate. The AOBs biotransform arsenite to arsenate with the regulation of ars genes, which detoxify highly As-contaminated mine.Conclusion:The AOBs from Samkwang mine are known for their resistance to highly toxic arsenic environments. They play a crucial role in the bioremediation of abandoned mines by transforming As(III) into As(V) through biotransformation.

SUSTAINABLE CONSTRUCTION MANAGEMENT SYSTEM

This paper presents a systematic approach to environmental management of pollution and/or hazards caused by construction projects. It proposes a qualitative approach to assess and control the problem and a method to calculate the Construction Pollution Index (CPI) which provides a quantitative measurement of pollution and/or hazards caused by the construction projects. Questionnaires were distributed to the organization and the results were made into percentage, level of readiness index and regression test to analyze the organizations responds. Results was analyzed through Statistical Package for the Social Sciences tools Environmental impact assessments (EIA) and environmental management systems (EMS) enable organizations to identify, access and manage environmental impacts throughout a development's lifecycle, improving overall environmental performance. Based on the analysis and discussions, the paper further proposes that major construction companies should obtain ISO 14001 Environmental Management System (EMS) certifications. Keywords: environmental management, hazards, Environmental impact assessments (EIA), environmental management systems (EMS).

Quantifying air quality co-benefits to industrial decarbonization: the local Air Emissions Tracking Atlas

IntroductionMany decarbonization technologies have the added co-benefit of reducing short-lived climate pollutants, such as particulate matter (PM), nitrogen oxides (NOx), and sulfur dioxide (SO2), creating a unique opportunity for identifying strategies that promote both climate change solutions and opportunities for air quality improvement. However, stakeholders and decision-makers may struggle to quantify how these co-benefits will impact public health for the communities most affected by industrial air pollution.MethodsTo address this problem, the LOCal Air Emissions Tracking Atlas (LOCAETA) fills a data availability and analysis gap by providing estimated air quality benefits from industrial decarbonization options, such as carbon capture and storage (CCS). These co-benefits are calculated using an algorithm that connects disparate datasets that separately report greenhouse gas emissions and other pollutants at U.S. industrial facilities.ResultsVersion 1.0 of LOCAETA displays the estimated primary PM2.5 emission reduction co-benefits from additional pretreatment equipment for CCS on industrial and power facilities across the state of Louisiana, as well as the potential for VOC and NH3 generation. The emission reductions are presented in the tool alongside facility pollutant emissions information and relevant air quality, environmental, demographic, and public health datasets, such as air toxics cancer risk, satellite and in situ pollutant measurements, and population vulnerability metrics.DiscussionLOCAETA enables regulators, policymakers, environmental justice communities, and industrial and commercial users to compare and contrast quantifiable public health benefits due to air quality impacts from various climate change mitigation strategies using a free and publicly-available tool. Additional pollutant reductions can be calculated using the same methodology and will be available in future versions of the tool.

Continuous Markov Models for Analyzing the Effect of Environmental Personal Exposure on Multiple Sclerosis Progression.

Multiple sclerosis (MS) is an inflammatory autoimmune demyelinating disorder of the central nervous system, leading to progressive functional impairments. Predicting disease progression with a probabilistic and time-dependent approach might help suggest interventions for a better management of the disease. Recently, there has been increasing focus on the impact of air pollutants as environmental factors influencing disease progression. This study employs a Continuous-Time Markov Model (CMM) to explore the impact of air pollution measurements on MS progression using longitudinal data from MS patients in Italy between 2013 and 2022. Preliminary findings indicate a relationship between air pollution and MS progression, with pollutants like Particulate Matter with a diameter of 10 micrometers (PM10) or 2.5 micrometers (PM2.5), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO) showing potential effects on disease activity.

Carbon based sensors for air quality monitoring networks; middle east perspective.

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

Measuring global monetary damages from particulate matter and carbon dioxide emissions to track sustainable growth

An integrated framework that tracks global stocks and flows of natural capital is needed to assess sustainable economic growth. Here, we develop a set of globally comprehensive monetary damages from particulate matter air pollution and greenhouse gas emissions in 165 countries from 1998 to 2018. Our results show that pollution intensity began to rise after a decade during which the global economy became less pollution-intensive from the late 1990s until the Great Recession. Larger economic production shares and higher pollution intensity in China and India drove this change. Deducting pollution damage from output from the late 1990s until the Great Recession yields higher growth estimates. After the Great Recession, this adjustment for pollution damage attenuated growth. We show that modeling monetary damages instead of physical measures of environmental quality affects inferences about sustainable development. Further, the monetary damages from exposure to particulate emissions peak earlier in the development path than damages due to carbon dioxide emissions. Monetary damages peak later than physical measures of both pollutants. For carbon dioxide, per capita emissions maximize at just over 60,000 dollars while monetary damages peak at nearly 80,000 dollars. In 2018, all but two countries were below this income level. Our results suggest that the global economy is likely to exhibit rising damages from particulates and carbon dioxide emissions in the years to come as nations grow and develop.

‘This smoke will finish us’: impacts of cooking with polluting fuels on air quality, health and education in three schools in Nairobi, Kenya

Links between cooking with polluting fuels (e.g. wood and charcoal), air pollution and health in domestic settings are well-established. However, few studies have been conducted in schools that rely on such fuels for catering. This study is the first investigation of air pollution, cooking, and health in schools in Nairobi, Kenya. We carried out an in-depth mixed-methods study in three schools (two primary schools and a college) in an informal settlement using wood and/or charcoal for catering. In each school, repeated 24-hour air pollution measurements (fine particulate matter (PM2.5) and carbon monoxide (CO)) were collected to assess concentrations in the main kitchen and a nearby classroom, in addition to personal exposure for the main cook. Surveys with catering staff collected data on perspectives on air pollution and health symptoms. Focus groups were conducted with catering staff, teachers and senior management to understand perceived impacts on health and the school environment. 24 hour levels of PM2.5 were found to surpass world health organization interim target level 1 (IT-1) (35 μg m−3) in all schools—with levels three times higher (107.6 μg m−3) in classrooms, ten times higher (316.2 μg m−3) in kitchens and nearly six times higher (200.9 μg m−3) among cooks. Peak levels of pollution were closely linked to times of stove use, as concentrations doubled in classrooms and tripled in kitchens during cooking. Catering staff reported being concerned about their health, and reported experiencing wheezing, chest pains, eye irritation and headaches and attempted to avoid the smoke to reduce exposure. Disturbance to classes from cooking smoke was reported by teachers, with students reporting coughing and sneezing from exposure. Support is needed to enable clean cooking transitions in schools to create a healthy and safe learning environment.

Association between synoptic types in Beijing and acute myocardial infarction hospitalizations: A comprehensive analysis of environmental factors

BackgroundEnvironmental factors like air pollution and temperature can trigger acute myocardial infarction (AMI). However, the link between large-scale weather patterns (synoptic types) and AMI admissions has not been extensively studied. This research aimed to identify the different synoptic air types in Beijing and investigate their association with AMI occurrences. MethodsWe analyzed data from Beijing between 2013 and 2019, encompassing 2556 days and 149,632 AMI cases. Using principal component analysis and hierarchical clustering, classification into distinct synoptic types was conducted based on weather and pollution measurements. To assess the impact of each type on AMI risk over 14 days, we employed a distributed lag non-linear model (DLNM), with the reference being the lowest risk type (Type 2). ResultsFour synoptic types were identified: Type 1 with warm, humid weather; Type 2 with warm temperatures, low humidity, and long sunshine duration; Type 3 with cold weather and heavy air pollution; and Type 4 with cold temperatures, dryness, and high wind speed. Type 4 exhibited the greatest cumulative relative risk (CRR) of 1.241 (95%CI: 1.150, 1.339) over 14 days. Significant effects of Types 1, 3, and 4 on AMI events were observed at varying lags: 4–12 days for Type 1, 1–6 days for Type 3, and 1–11 days for Type 4. Females were more susceptible to Types 1 and 3, while individuals younger than 65 years old showed increased vulnerability to Types 3 and 4. ConclusionAmong the four synoptic types identified in Beijing from 2013 to 2019, Type 4 (cold, dry, and windy) presented the highest risk for AMI hospitalizations. This risk was particularly pronounced for males and people under 65. Our findings collectively highlight the need for improved methods to identify synoptic types. Additionally, developing a warning system based on these synoptic conditions could be crucial for prevention.

Predicting air quality index using attention hybrid deep learning and quantum-inspired particle swarm optimization

Air pollution poses a significant threat to the health of the environment and human well-being. The air quality index (AQI) is an important measure of air pollution that describes the degree of air pollution and its impact on health. Therefore, accurate and reliable prediction of the AQI is critical but challenging due to the non-linearity and stochastic nature of air particles. This research aims to propose an AQI prediction hybrid deep learning model based on the Attention Convolutional Neural Networks (ACNN), Autoregressive Integrated Moving Average (ARIMA), Quantum Particle Swarm Optimization (QPSO)-enhanced-Long Short-Term Memory (LSTM) and XGBoost modelling techniques. Daily air quality data were collected from the official Seoul Air registry for the period 2021 to 2022. The data were first preprocessed through the ARIMA model to capture and fit the linear part of the data and followed by a hybrid deep learning architecture developed in the pretraining–finetuning framework for the non-linear part of the data. This hybrid model first used convolution to extract the deep features of the original air quality data, and then used the QPSO to optimize the hyperparameter for LSTM network for mining the long-terms time series features, and the XGBoost model was adopted to fine-tune the final AQI prediction model. The robustness and reliability of the resulting model were assessed and compared with other widely used models and across meteorological stations. Our proposed model achieves up to 31.13% reduction in MSE, 19.03% reduction in MAE and 2% improvement in R-squared compared to the best appropriate conventional model, indicating a much stronger magnitude of relationships between predicted and actual values. The overall results show that the attentive hybrid deep Quantum inspired Particle Swarm Optimization model is more feasible and efficient in predicting air quality index at both city-wide and station-specific levels.

A study and development of high‐order fuzzy time series forecasting methods for air quality index forecasting

AbstractThe endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high‐order fuzzy TSF methods, TFS‐multilayer perceptron (MLP) and HFS‐MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long‐short term memory (LSTM), and convolutional neural network (CNN). The “Wilcoxon Signed‐Rank test” and “Friedman and Nemenyi hypothesis test” are applied to the results obtained by employing different ratios in the train‐validation‐test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS‐MLP method over all other crisp and fuzzy TSF methods employed in this paper.

MITIGATION EFFORTS FOR QUICK DETECTION OF POLUTAN GAS AT PUTRI CEMPO TPA USING PROTOTYPE ZnOTiO2GO

The impact of the increasing volume of waste in the Putri Cempo TPA causes the emergence of pollutants in the form of liquid, gas or solid. Gas is a dangerous pollutant because it can cause respiratory disease and even death. methane gas (CH4), sulfurdioxide (SO2), ammonia (NH3), and hydrogen sulfide (H2S), so that it can be the cause of respiratory (respiratory)-related diseases cardiovascular, and physiological changes such as pulmonary function and blood pressure. This study tries to identify pollutant gases that arise in the Putri Cempo TPA and the detection method uses ZnO-TiO2-GO Material which previously used an adsorbent solution. The use of this material has the advantages of being practical, safer and easier to recycle. The gas identification carried out includes SO2, O3, NO2, H2S and NH3 gases. ZnO-TiO2 and GO materials were varied into variation A, B, C ,D And E. The results showed that The results show that the composition of TiO2; ZnO Graphene C (45%; 45%, 10%) shows better results for detecting NO2 gas than the p-aminodimethylaniline working solution used for laboratory measurements. The results show that the composition of TiO2; ZnO Graphene E (60%; 30%, 10%) shows the composition's ability to detect 10 times more H2S gas than the p-aminodimethylaniline working solution but the ability to detect NH3 is lower than the p-aminodimethylaniline working solution. composition variations in TiO2; ZnO ; Graphene B (30%; 60%, 10%), and D (80%; 15%, 5%) can detect exposure to SO2 and O3 gases 6 times more than p-aminodimethylaniline solution. From various compositions, it shows that TiO2, ZnO, Graphene materials are more dominant than aminodimethylaniline solution except for the detection of NH3 pollutant. The results of the research recommendations need to be developed prototypes for the detection of the five pollutant gases without having to go through laboratory testing. The development of a prototype of the pollutant gas detection tool can speed up the measurement of gas pollutants. So that it is easier for analysts to measure gas pollutants. It is necessary to develop the effect of the concentration of adsorbed pollutant gas on the composition of materials with various conductivity of material compositions for further research

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

TROPESS-CrIS CO single-pixel vertical profiles: intercomparisons with MOPITT and model simulations for 2020 western US wildfires

Abstract. The new TROPESS (TRopospheric Ozone and its Precursors from Earth System Sounding) profile retrievals of carbon monoxide (CO) from the Cross-track Infrared Sounder (CrIS) are evaluated against Measurement of Pollution in the Troposphere (MOPITT) CO version 9 data. Comparison results that were adjusted to common a priori constraints in the retrieval processes have improved agreement between the two data sets over direct comparisons. TROPESS-CrIS CO profiles are within 5 % of MOPITT but have higher concentrations in the lower troposphere and lower concentrations in the upper troposphere. For the intense western US wildfire events in September 2020, we compare CO fields simulated by the GISS climate model to the two satellite CO observations. We show intermediate steps of the comparison process to illustrate the evaluation of model simulations by deriving the “retrieved” model CO profiles as they would be observed by the satellite. This includes the application of satellite level-2 data along with their corresponding diagnostic operators provided in the TROPESS-CrIS and MOPITT products. The process allows a diagnosis of potential model improvements in modeling fire emissions and pollution transport.