Pollutant Concentrations Research Articles

Assessment of Indoor Air Quality and Influential Factors: A Study in Rural Areas of Solan District, Himachal Pradesh (India)

The study was conducted during December, 2020 to June, 2021 to assess indoor air quality (IAQ) and develop amelioration strategies in rural areas of the Solan district, located in Himachal Pradesh, India. A well-structured pre-tested questionnaire was used to assess the factors responsible for indoor air quality.The indoor air quality parameters including PM10, Non-Respirable Suspended Particulate Matter (NRSPM), Total Suspended Particulate Matter (TSPM), Nitrogen dioxide (NO2) and Sulfur dioxide (SO2)were studied to calculate air quality index.The findings revealed substantial variation in pollution levels among different villages. Nauni village had the highest concentration of pollutants, with PM10 levels at 24.17 µg m3-1, NRSPM at 20.37 µg m3-1, TSPM at 44.54 µg m3-1, NO2 at 12.54 µg m3-1 and SO2 at 0.35 µg m3-1. In contrast, Nagali village has the lowest levels of these contaminants. Despite variations, all recorded values were within permissible limits. Nine factors were identified as contributing to poor indoor air quality: fuel wood-based cooking, smoke sources, mode of ventilation, insect/pest control measures, waste disposal near the house, use of perfumes and air freshners, cowshed near the house, intermittent cleaning, and dampness in the house due to leakage. To reduce indoor air pollution in rural areas, the study suggests establishing concentrated methods that address these specific issues. Proper ventilation, alternate cooking methods and better waste management procedures are important steps in improving indoor air quality and protecting public health.

Sustainable Activated Carbon from Agricultural Waste: A Study on Adsorption Efficiency for Humic Acid and Methyl Orange Dyes

In this study, porous activated carbon was produced from coffee waste and used as an effective adsorbent for the removal of humic acid (HA) from seawater and methyl orange (MO) dye from aqueous solutions. Phosphoric acid H3PO4 was used as an activating agent for the chemical activation of these agricultural wastes. The characterization of the activated carbon obtained using a scanning electron microscope (SEM), Fourier-transform infrared (FTIR) spectroscopy analysis, X-ray diffraction (XRD) and the Brunauer–Emmett–Teller (BET) method revealed that the activated carbon products exhibited high porosity and the formation of various functional groups. The effects of different parameters were examined using batch adsorption experiments, such as the adsorbent masses, pH, initial pollutant concentration and contact time. The results show that the performance increased with an increased adsorbent mass (up to 0.25 g/L) and decreased initial concentration of the adsorbent tested. On the other hand, this study clearly showed that the adsorption efficiency of the MO on the raw spent coffee grounds (SCGs) waste was around 43%, while no removal was observed for the humic acid. The experiments demonstrated that the activated carbon synthesized from the used coffee grounds (the efficiency was compared with commercial activated carbon (CAC) with a difference of 13%) was a promising alternative to commercially available adsorbents for the removal of humic acid from seawater. To understand and elucidate the adsorption mechanism, various isothermal and kinetic models were studied. The adsorption capacity was analyzed by fitting experimental data to these models. The experimental data for methyl orange dyes were analyzed using Langmuir and Freundlich isothermal models. The Freundlich isotherm model provided a superior fit to the equilibrium data, as indicated by a higher correlation coefficient (R2) than that of the Langmuir model. The maximum adsorption was observed at pH 3. The Freundlich adsorption capacity was found to be 333 mg/g adsorbent. The PAC showed a high adsorption capacity for the MO and HA. The PAC showed the highest adsorption capacities for the HA and MO compared with the other adsorbents used (SCGs and CAC) and would be a good material to increase the adsorption efficiency for humic acid removal in the seawater pretreatment process. In addition, the prepared AC BET surface area was 520.40 m2/g, suggesting a high adsorption capacity. This makes the material potentially suitable for various applications that require a high surface area. These results indicate that high-quality sustainable activated carbon can be efficiently produced from coffee waste, making it suitable for a wide range of adsorbent applications targeting various pollutants.

Boosting ciprofloxacin and indigo carmine dye photodegradation using S-scheme plate-like BiOI@sulfated TiO2 heterojunctions: An understanding of the performance, the degradation route, and DFT computation

Sustainable BiOI/sulfated TiO2 nanocomposites were created for the present study utilizing the environmentally friendly dispersion-ultrasonication technique. Then, their ability to degrade the medication ciprofloxacin (CIP) and indigo carmine coloring (IC) in aquatic water was evaluated. The optimized catalyst 10%BiOI/ST, denoted as 10BOST together with 5BOST, is subjected to thorough characterization together with that of sulfate-free TiO2 (10BOT) to assess their physiochemical, morphological, textural, structural, and elemental composition properties. It is noteworthy that the 10BOST composite achieves remarkable degradation and performs exceptionally well in photocatalytic IC and CIP degradation. In 35 min, it degrades IC to 100%, with a rate constant of 0.066 min−1, which is superior to that of 10BOT (0.054 min−1) in the incidence of visible light and without an oxidizing agent. In CIP, 10BOST yields 85.6% degradation with a rate constant of 0.027 min−1 preceding 0.024 min−1 for 10BOT when potassium persulfate oxidizing agent is present. This exceptional performance is ascribed to the composite's diminished particle diameter, largest pore radius, hydrophilicity, improved light absorption, and the developed heterojunction between ST and BiOI, as demonstrated by XPS, TEM, XRD, EDX and IR data. The effect of sulfation concentration, pH, Ti3+/Ti4+ ratio, and pollutant concentration were studied together with the active species determinations, which revealed that active radicals like SO4•−, •OH, and h+ were well participated in the pollutant's decomposition. Testing for toxicity shows that during CIP breakdown, innocuous hazardous intermediates evolve with a notable 70% mineralization rate in their TOC. Furthermore, 10BOST showed strong stability and reusability. Using DFT simulations, profound insights into the principles underlying the adsorption sites of pollutants, their chemical reactivity, and the arrangement of their electrical charges, and how these factors affect the reaction mechanism on BOST photocatalysts.

Air Quality Benefits of Renewable Energy: Evidence from China’s Renewable Energy Heating Policy

This paper examines the impact of renewable energy heating on air quality in China, using the Qinling Mountains–Huaihe River line as a quasi-natural experiment to distinguish between regions with central heating and those without. Employing a difference-in-differences approach and analyzing panel data from 298 cities between 2014 and 2022, our findings indicate that the renewable energy heating policy has significantly improved air quality. Specifically, the policy led to substantial improvements in air quality, reducing concentrations of key pollutants: SO2 by 28.31%, CO by 7.57%, NO2 by 5.72%, and PM2.5 by 7.15%. The policy’s effects are most pronounced in regions with lower temperatures and in the eastern parts of the country. Further analysis emphasizes the critical role of energy transition, environmental regulations, and government investment in technology as key drivers of these air quality improvements.

Identifying the distribution and source of riverine plastic waste contamination: case study of Brantas River in Malang city

Brantas River experiences significant pollution due to domestic and industrial wastes. This condition also causes natural disasters such as floods. Single use plastics generally made up a large portion of local wastes. However, previous studies mostly investigated plastic waste pollution and local waste management habits as separate topics. Therefore, the objects of this study are the plastic wastes in Brantas River, as microplastic particles, and the identification of its source. The assessments were based on microplastic counts, macroinvertebrate bioassessment with SIGNAL-2 index, and the quantitative data of local domestic waste management at three Stations in Malang City area. Riverine microplastic pollutant concentrations and its sources were both successfully identified and solved. The results showed that the highest riverine microplastic particles were found in Station 3. This station only had four macroinvertebrate taxa with a SIGNAL-2 score of 4.42, indicating severe degradation. Quantitative data showed that 80 % of Station 3 residents threw plastic wastes directly to Brantas River. The low macroinvertebrate counts possibly caused by ingestion of microplastic from households and small-scale enterprises which heavily utilized single use plastics to trade their goods. The first distinctive feature of this study is the comparability of the microplastic, and macroinvertebrate counts with the river degradation status. Secondly, the quantitative data can serve as complementary evidence. Practically, the obtained results can be developed into an integrated plastic waste management plan for the residents surrounding the rivers, particularly in developing countries with similar socio-cultural conditions as elaborated in this study, to maintain the ecosystem quality

Research and Prediction of China's Air Environment

Abstract: With the rapid development of China's economy, environmental issues, particularly air pollution, have become increasingly severe, posing significant threats to human health and ecosystems. This study employed statistical learning methods to analyze air pollution composition data from thirty major Chinese cities throughout 2019-2022. The study used algorithms like regression, classification, fitting, and filtering to find changes in multi-source heterogeneous data patterns and predict how air pollutant concentrations would change over time and space. At the same time,the results indicate that the primary factors influencing the Air Quality Index (AQI) are fine particulate matter (PM2.5) and inhalable particulate matter (PM10). Additionally, this research is significant for improving public health, advancing environmental protection, fostering economic growth, strengthening laws and regulations, ensuring social stability, and promoting scientific and cultural advancements. Through this research, scientific bases can be provided for formulating policies that reduce disease, enhance government credibility, drive innovation in atmospheric research, and preserve cultural heritage.

Vertical distribution of ambient air pollutants (PM2.5, PM10, NOX, and NO2); A systematic review

BackgroundNumerous investigations have explored variations in vertical air pollutant concentrations, yielding diverse findings. So, we have conducted this systematic review to gain a more comprehensive understanding of the vertical distribution of air pollutant concentrations (PM2.5, PM10, NOX, and NO2) and the influencing factors. MethodsRelevant studies were identified by searching the three central electronic databases, PubMed, Web of Science, and Scopus, from the beginning of 2000 to the end of 2023. This study included original articles published in English that examine the pollutant concentration variations below 500 m. FindingOf 3614 articles, 57 studies met our criteria. Our findings showed a decline in PM2.5 and NOX concentrations as altitude increased, while NO2 concentrations exhibited an increase. Conversely, no statistically significant relationship was identified between altitude and PM10 concentrations. The regression analysis yielded coefficients for the relationship between concentration and altitude (0–500 m) as follows: PM2.5 (−0.11), PM2.5/PM10 (0.0008), NOX (−0.11), and NO2 (0.13). Conducting additional research on this topic and investigating the impact of meteorological parameters on pollutant concentrations at different altitudes enhances our understanding of the vertical distribution of pollutant concentrations.

The Formaldehyde, TVOCs, PM2.5 and PM10 Concentrations at Outdoor Street Food in Kalasin, Thailand

Human health and well-being are directly impacted by air quality. Cardiovascular and respiratory problems have been linked to exposure to pollutants such as formaldehyde, total volatile organic compounds (TVOCs), and particulate matter 2.5 and 10 micrometers (PM2.5 and PM10). This study aimed to quantify formaldehyde, TVOCs, PM2.5, and PM10 levels in the outdoor air at street food vendors in Kalasin, Thailand. The study was conducted at street food vendors in the province of Kalasin. Formaldehyde values at the top five food stalls in Kalasin ranged from 0.58 to 1.41 mg/m3. The PM2.5 values ranged from 2.29 to 9.43 ug/m3, and the TVOC values ranged from 2.91 to 7.02 ug/m3, with PM10 concentrations ranging from 2.86 to 7.29 ug/m3. Drawing on data about these pollutant concentrations across five distinct locations, the following recommendations can be put forth: Elevated levels of formaldehyde, TVOCs, PM2.5, and PM10 in certain areas can have detrimental effects on the environment and human well-being.

Air Pollution Assessment of Thermal Power and Cement Plants in Ninh Binh Province

Cement production and thermal power generation are two primary industries in Ninh Binh province, but they are also among the sources of emissions that can negatively affect to local and regional air quality. This study employs the AERMOD air dispersion modeling system to assess the air pollution levels caused by the operations of the cement and thermal power plants in Ninh Binh province. The results show that the highest average hourly concentrations of TSP, SO2, and NOx are 30.1 µg/m3, 73.2 µg/m3, and 361 µg/m3, respectively; the highest average daily concentrations are 3.77 µg/m3, 2.17 µg/m3, and 49.0 µg/m3, respectively. The predominant direction of pollutant dispersion is towards the West and Southwest, affecting areas within Ninh Binh City and Tam Diep City. The AERMOD model results indicate that pollutant concentrations are lower than the relevant national technical standards. However, it is concerning that a small region within Cuc Phuong National Park shows signs of being affected by these air pollutants. Therefore, stringent control measures on emissions from thermal power and cement plants must be in place to ensure the local air quality and public health.

Low-temperature treatment optimization for diesel-contaminated kaolin: Mutual impacts of generated pyrolytic carbon and particle agglomeration

Efficiency improvement in low-temperature treatment for diesel-contaminated sites is urgent because changes in soil properties and the generation of new substance during the remediation process can influence the duration and energy utilization. This paper focuses on low-temperature treatment optimization based on the mutual impacts of pyrolytic carbon and kaolin aggregation. Results reveal that the peak mass loss rate occurred between 100 and 150 °C, with minimal loss beyond 200 °C. Samples thermally treated at 150–200 °C exhibited darker colors, indicating pyrolytic carbon formation, corroborated by x-ray photoelectron spectroscopy (XPS), fourier transform infrared spectroscopy (FTIR), and three-dimensional fluorescence spectrum (3D-EEM) analyses. Additionally, diesel contamination influenced the fractal dimension of aggregates by influencing adhesion forces (<10000 mg/kg) and forming liquid bridges (≥10000 mg/kg) in untreated kaolin, resulting in an initial increase and subsequent fall in fractal dimension with increasing concentration. Decline rates of pollutant gas concentration were closely correlated with fractal dimension changes under thermal conditions due to pollutant volatilization and pyrolytic carbon formation. Based on the consistency between fractal dimension and decline rate, two critical remediation concentrations (C0) and temperatures (T0) indices were identified to optimize the low-temperature remediation.

The effect of air pollution on hospitalizations with Parkinson’s disease among medicare beneficiaries nationwide

We examined the effect of annual exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3), on the rate of first hospitalization with a PD-related diagnosis (hospitalization with PD) among Medicare Fee-for-Service beneficiaries (2001-2016). Machine learning-derived annual air pollution concentrations were linked to residential ZIP codes. For each exposure, we fitted four models: 1) traditional outcome stratification, 2) marginal structural, 3) doubly robust, and 4) generalized propensity score matching Poisson regression models, adjusted for sociodemographic and meteorological confounders and long-term trends. Among 49,121,026 beneficiaries, incidence rate ratios of 1.08 (95% CI: 1.07, 1.10), 1.07 (95% CI: 1.05, 1.08), and 1.03 (95% CI: 1.02, 1.05) for an interquartile range increase in PM2.5 (3.72 µg/m3), NO2 (13.84 ppb), and O3 (10.09 ppb), respectively, were estimated from doubly robust models. Results were similar across modeling approaches. In this nationwide study, higher air pollution exposure increased the rate of hospitalizations with PD.

Preparation, characterization and application of polymeric ultra-permeable biodegradable ferromagnetic nanocomposite adsorbent for removal of Cr(VI) from synthetic wastewater: kinetics, isotherms and thermodynamics

Hexavalent chromium (Cr(VI)) contamination in drinking water due to industrial activities is a growing worldwide concern. Cr(VI) concentrations exceeding a few parts per billion (ppb) can cause serious health problems such as asthma, blood cancer, kidney-related diseases, liver and spleen damage, as well as neurological system, immunological deficiencies, and reproductive issues. This study, thus, explored the feasibility of employing a novel polymeric ferromagnetic nanocomposite adsorbent made of low-cost, biodegradable, and ultra-permeable materials from pulp and paper sludge for adsorptive removal of hexavalent chromium (Cr6+) from synthetic wastewater. Vibrating-sample magnetometer (VSM), X-ray diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmet-Teller surface area (BET), and Fourier transform infrared (FTIR) were used to analyze the produced nanocomposite adsorbent. The Fourier transform infrared results confirmed the presence of adsorptive peaks attributed to −OH, −NH2, and FeO. Scanning electron microscopy micrographs revealed a porous adsorbent surface. XRD revealed the existence of the crystalline spinel-structured magnetite (Fe3O4) phase of iron oxide, while the saturation magnetization was established to be 26.90 emu/g. The Brunauer–Emmett–Teller analysis confirmed a slight decrease in the surface area of the nanocomposite adsorbent to 6.693 m2.g−1, compared to Fe3O4 (7.591 m2.g−1). The optimum conditions for Cr6+ removal were pH 2.0, 1.0 g/L adsorbent dose, room temperature (25°C), 120 min contact time, and 20 mg/L pollutant concentration. During removal, the Cr(VI) was adsorbed by electrostatic attraction and/or reduced to trivalent chromium Cr(III). At low starting Cr(VI) concentrations, chemisorption dominated the removal process, but as concentrations increased, physisorption became more significant. The prepared nanocomposite adsorbent presented exceptional removal efficiency of up to 92.23%, indicating that it may be useful for the adsorption of metal ions from industrial and household wastewater.

Effects of the vehicle fleet on air quality in an urban university environment of Ecuador

Introduction: Air quality in university environments is critical for the health and well-being of students and staff. This study aims to identify the permissible pollutants emitted in the parking lots of the State Technical University of Quevedo (UTEQ) in the canton of Quevedo, province of Los Ríos, Ecuador. Methods: An abductive (inductive-deductive) approach was employed, utilizing statistical methods to analyze primary emission data. In-situ measurements of pollutants, specifically carbon monoxide (CO) and particulate matter (PM2.5 and PM10), were conducted monthly across three parking lots at the university. Each parking lot contained three strategically established monitoring points, where measurements were taken during peak entry and exit hours and on days of highest traffic, using the Temtop M2000C-2º sensor. Results: The findings revealed significant statistical differences in pollutant concentrations among the parking lots. CO levels were notably higher in the main parking lot during midday, while PM2.5 and PM10 exhibited greater dispersion in the rear parking lot. The maximum measured values were 456.71 ppm (CO) in the main parking lot, 22.29 µg/m³ (PM2.5) in the Produteq lot, and 32.54 µg/m³ (PM10) in the rear parking lot, all of which remain within the permissible limits established by Ecuadorian legislation. Conclusion: Although air pollution levels at UTEQ are classified as moderate, the results underscore the necessity for implementing short-term mitigation measures to enhance air quality. It is recommended that university authorities develop projects and actions aimed at reducing vehicular emissions and improving overall environmental conditions on campus.

How to use efficiently airborne criteria pollutants and radon-222 in source apportionment: A self-organizing maps approach

Pollutant source apportionment represents one of the fundamental activities in environmental science. Several efficient chemometric tools are available to the scope, mostly based on multivariate techniques and usually applied to aerosol chemical speciation data. In the present work, an alternative source profiling method is proposed, based on the self-organizing maps (SOM) algorithm. Moreover, the dataset used includes typical criteria pollutants and physical parameters related to airborne particulate matter widely used as a complement of aerosol source apportionment and largely available at a higher time resolution than bulk aerosol samplings, allowing the information on the dynamic behavior of the local airshed to be extended. In this work, data was collected at a coastal location in NW Italy, between January and July 2012. Hourly concentrations of typical gaseous pollutants (SO2, NO, NO2, benzene, toluene, (m-p)-xylene, o-xylene), black-carbon and particle number concentrations by an optical particle sizer (OPS) were collected. The dataset was integrated with radon-222 activity concentration and meteorological parameters to enrich and refine the information obtained by SOM computation as well as to improve the air pollution source localization. Despite the lower specificity of criteria pollutants, the approach developed was capable of revealing distinct pollution sources such as the urban background traffic, the coal-fired power plant active at the time of the study, and the harbor, in agreement with previous PM-based source apportionment studies carried out locally, while enlightening peculiar dynamical conditions detectable at the sub-daily time scale. The application of the SOM algorithm, with the integration of meteorological parameters and atmospheric radon, proved to be very efficient in unveiling the air pollution sources.

Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning

Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co-exposure to MNPs and HMs on plants, which may be related to various influencing factors. In this study, existing publications were searched and analyzed using CiteSpace, meta-analysis, and machine learning. CiteSpace analysis showed that this research field was still in the nascent stage, and hotspots in this field included accumulation, cadmium (Cd), growth, and combined toxicity. Meta-analysis revealed the differential association of seven influencing factors (MNP size, pollutant treatment duration, cultivation media, plant species, MNP type, HM concentration, and MNP concentration) and 8 physiological parameters receiving the most attention. Co-exposure of the two contaminants had stronger toxic effects than HM treatment alone, and phytotoxicity was generally enhanced with increasing concentrations and longer exposure durations, especially when using nanoparticles, hydroponic medium, dicotyledons producing stronger toxic effects than microplastics, soil-based medium, and monocotyledons. Dry and fresh weight analysis showed that co-exposure to MNPs and Cd resulted in significant phytotoxicity in all classifications. Concerning the MNP types, polyolefins partially attenuated plant toxicity, but both modified polystyrene (PS) and biodegradable polymers exacerbated joint phytotoxicity. Finally, machine learning was used to fit and predict plant HM concentrations, showing five classifications with an accuracy over 80 %, implying that the polynomial regression model could be used to predict HM content in plants under complex pollution conditions. Overall, this study identifies current knowledge gaps and provides guidance for future research.

Magnetic recyclable carboxymethyl cellulose/gelatin/citrate@Fe3O4 photo-nanocomposite beads for ciprofloxacin removal via hybrid adsorption/photocatalysis process under solar light as a renewable energy source

Magnetically separable cross-linked carboxymethyl cellulose/gelatin/citrate-functionalized magnetite nanoparticles (Cit-Fe3O4) photo-nanocomposite beads (mCMC/Ge) were synthesized and applied in synergistic adsorption/photocatalytic degradation of ciprofloxacin (Cipro) pharmaceutical pollutant under sunlight irradiation. Various analytical techniques were employed to characterize their structural, textural, magnetic, thermal, and optical properties. The removal efficiency of mCMC/Ge beads was investigated considering different influencing parameters (pH, beads dosage, contact time, Cipro concentration, and temperature). Experimental data modeling indicated that the adsorption process followed pseudo-second-order kinetics and Langmuir isotherm models, with a maximum Langmuir adsorption capacity (qm) of 50 mg g−1 for mCMC/Ge, twice that of the matrix. Photocatalytic activity results showed prominent enhancement in Cipro removal using 1 g L−1 of mCMC/Ge at pH 7, as compared to Cit-Fe3O4, reaching 96 %, 85 %, and 63 % after 180 min of adsorption and 120 min of irradiation for initial pollutant concentrations of 10, 20, and 60 mg L−1, respectively. Furthermore, mCMC/Ge demonstrated efficient removal even in real water sample.The excellent removal performance of mCMC/Ge highlighted the synergy between polymeric matrix template and encapsulated Cit-Fe3O4 in improving Cipro adsorption and photodegradation. Furthermore, facile recyclability and sustained activity over five cycles identify mCMC/Ge photo-nanocomposite as a promising material for removing organic pollutants from contaminated waters.

Air quality disparities and respiratory health risks in critically polluted and relatively non-polluted areas: a prospective child health study.

Air pollution, a significant global health concern, notably impacts human well-being. Children, owing to their distinctive physiology and behavior, are particularly susceptible to its adverse effects. This prospective study examines air quality variations and respiratory risks in children residing in critically polluted areas (CPA) compared to relatively non-polluted areas (NPA), utilizing a prospective design to understand the impacts of air pollution on children's respiratory health, including measures like relative risk (RR) and attributable risk (AR). This prospective study tracked 739 students of 5th- 7th grade residing in CPA and NPA for one year, and measured the ambient and indoor air quality levels in both these areas. Throughout the study, based on the observed respiratory symptoms new episodes of upper and lower respiratory tract illnesses were recorded for each child on a weekly basis. Incidence rate, RR and AR for both the illnesses were compared. Statistical analysis was performed with SPSS 26.0. The study observes higher concentration for particulate matter and gaseous pollutants at CPA in comparison to NPA. Children living in CPA exhibited a notably greater weekly occurrence of both upper and lower respiratory tract illnesses compared to those in NPA, with RR of 1.26 (95% CI: 1.16-1.37) and 1.74 (95% CI: 1.34-2.27), respectively. The AR associated with air pollution for upper and lower respiratory tract illnesses among CPA students was found to be 20.7% and 42.7%, respectively. This research underscores the pressing need to address air pollution in critically polluted areas and its profound effects on children's respiratory health. Public health interventions, such as reducing emissions from industries and creating green spaces, should be prioritized. Furthermore, early respiratory health screening in schools within polluted areas could aid in the timely diagnosis and management of respiratory issues in children.

The effect of temperature on the Emission Factor of air emissions from pyrolysis of used tyres

Used tyres pyrolysis is an efficient solid waste management strategy for used tyres and a growing method of fuel oil production. However, the impact of air emissions associated at different temperature on ambient air quality has not been determined. To solve this problem, a pyrolysis fixed bed reactor and a condensing unit for separating liquid from produced gas were designed and constructed. 0.20 kg of used tyres were pyrolysed in the reactor and air emissions from the reactor chamber was characterized for air pollutants using the E-instrument E8500. Using a combination of measured mass of pollutants, mass of tyre pyrolzed and time taken during pyrolysis, Emission Factors of the air pollutants were established from the study at 200 °C, 300 °C and 400 °C. The concentration measured between 200 °C - 400 °C were in the range of 15 -7497 mg/m3, 0 – 4680 mg/m3, 38.25 – 103, 277.76 mg/m3 and 2 – 293 mg/m3 for CO, NOx, HC and H2S respectively. The developed emission factors were in the range of 16.52 – 8370.04 g kg-1, 0 – 4470.26 g kg-1, 42.126 – 104,055.9 g kg-1 and 2.20 – 322.69 g kg-1. The designed and constructed pyrolysis reactor and condenser unit were effective for used tyre pyrolysis while the measured air pollutants concentration were higher for all pollutants except H2S using the Federal Ministry of Environment (FEPA, 1991) standard. Hence, it is necessary to carry out further studies on air pollution control from waste tyre pyrolysis at lower temperature.

A predictive model for the estimation of industrial [formula omitted] emissions for IoT-based devices

The paper is devoted to solving the problem, associated with increasing the forecasting accuracy of pollutant concentration level based on PM2.5 dust. The LANN model (LANN – Lagrange powered Artificial Neuron Network), proposed in the paper, allows you to take into account the mutual influence of pollution sources on the concentration level at monitoring points, which, on the one hand, allows to manage the volume of emissions from industrial enterprises in such a way as to avoid penalties and, on the other hand, allows the regulatory agencies to determine points for environmental control, at which permissible standards will be most likely exceeded. The suggested solution makes it possible to use low-cost IoT-based air quality monitoring devices, which were primarily considered not applicable due to their low accuracy and exposure to the influence of short-term stochastic factors; besides, no models were available, which would be capable to provide the necessary accuracy and forecast horizon on the base of IoT data. At the same time, the obtained model has low requirements for computing resources compared to dispersion models and is dependent on the accuracy of weather information; besides, the model allows you to take into account trends and obtain better forecast values in terms of accuracy than the models, that use regression methods, neural network models and ML models, stochastic models and their ensembles. The application of the model improves well-known pollutant dispersion estimation calculation techniques through the use of measurement data flow and dynamic adaptation to changing conditions. The model was trained on the dataset over the period 2022–2023 collected from 5 monitoring devices, which were installed in the catchment area of the group, incl. 13 industrial points of PM2.5 pollutant sources.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.