Change in the concentration of pollutants in the air over the city of Lucknow, together with HYSPLIT4.0's trajectory and dispersion analysis

Change in the concentration of pollutants in the air over the city of Lucknow, together with HYSPLIT4.0's trajectory and dispersion analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Change in the concentration of pollutants in the air over the city of Lucknow, together with HYSPLIT4.0's trajectory and dispersion analysis Vipin Kumar, Prabhat Kumar Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4295589/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the mean daily concentrations of oxides of carbon (CO), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and respirable suspended particles (RSPM-PM 10 ) at seven monitoring stations in Lucknow city: Hazratganj, Gomti Nagar, Talkatora, Aliganj, Sarai Mali Khan, Mahanagar, and Ansal TC. The data is collected between 2016 and 2022. Based on the analysis, it can be observed that at all locations, the yearly average concentration of RSPM varies between 148.74 and 323.05 µg m -3 , SO 2 between 7.11 and 8.94 µg m -3 , NO 2 between 23.52 and 31.86 µg m -3 , and CO between 27.38 and 33.89 µg m -3 . The study of seasonal variation reveals that the lowest recorded concentration of RSPM was 81.59 µg m -3 during the monsoon, while the highest recorded concentration was 447.47 µg m -3 during the post-monsoon. On the other hand, seasonal variations in SO 2 and NO 2 were found to be below the recommended requirements, ranging from 5.55 to 10.94 µg m -3 and 20.23 to 38.40 µg m -3 , respectively. The COVID-19 lockout in 2020 caused the pollution level to somewhat decline, but it did not fall below the recommended limit for CO and RSPM. The Indian government has banned factories and implemented various measures within the city, but the levels of CO and PM 10 in Lucknow are not decreasing. The Trajectory and Dispersion study of the HYSPLIT4.0 model indicates that the wind, which blows from the northwest, carries pollutants from close by areas to maintain daily pollution levels above the Central Pollution Control Board's guidelines (i.e., 100 µg m -3 ). This suggests that there is insufficient local pollution control. The results suggest that controlling particulate matter and carbon monoxide pollution in the city is a serious challenge and has an alarming situation as compared to SO 2 and NO 2 pollutants. HYSPLIT4.0 Vehicular emission PM2.5 SO2 NO2 CO Lucknow Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Everything that exists on Earth depends on water and air which are also a vital resource for maintaining ecosystems, human health, and economic growth in appropriate amounts and of high quality (Patel et al. 2022 , 2023a , 2023b ). But the world's population is growing, urbanization is happening, and farming methods are changing. As a result, air pollution and wastewater output are rising significantly, endangering human health and the environment (P. Kumar et al. 2023 ; Patel et al. 2024 ). Either correctly handled or improperly treated wastewater can damage aquatic environments, taint streams, and spread illnesses that are spread by water. In response to these problems, wastewater treatment has emerged as a crucial element of environmental operations, helping to reduce the adverse effects of releases of wastewater on ecosystems and human health. Air pollution is a severe problem with the environment that has an impact on people's quality of life, the state of the environment, and overall wellness. One of the primary causes of air pollution is vehicle emissions, particularly in cities where traffic congestion is a daily occurrence. When fossil fuels are used in automobiles, a complex cocktail of pollutants is released, such as particulates (PM), nitrogen oxides (NOx), sulfur oxides (SOx), the gas carbon monoxide (CO), and other hazardous pollutants. These substances cause major threats to both human health and the environment. The capital of Uttar Pradesh is Lucknow Megacity, which has a population of 2.82 million according to 2011 data. Its area is 310 km 2 , and its coordinates are 26° 52' N latitude to 80 ° 56' E longitude, or 128 cadence above the ocean. Urban development has made the transport sector the focus of attention over the past 10 years, along with energy. In India, the transport industry uses around 16.9% of the country's total energy or 36.5 million tonnes of oil equivalent. Two-wheelers have seen a remarkable increase in proportion across all motor vehicle categories; they currently make up 70% of all motor vehicles in India (Ilarri et al. 2022 ). The main source of particulate air pollution in Lucknow is vehicle emissions. As of March 31, 2020, there were 34,24,478 car orders registered with the Regional Transport Office (RTO) in Lucknow, which represents a 13.35 gain over the previous period. On some of Lucknow City’s roads, the Uttar Pradesh State Road Transport Corporation (UPSRTC) launched machine services under the name “Lucknow Mahanagar Parivahan Sewa.” Given that air pollution in urban areas is mostly caused by motor vehicle emissions (traffic) (Biramo and Mekonnen 2022 ; Kovács et al. 2021). There is a major route close to Hazarat Ganj, Mahanagar, Talkatora, Aliganj, Sarai Malikhan, ATC, and Gomti Nagar that sees a lot more traffic than other roads do year-round. A regulatory framework is required for the management of traffic, air quality, and emissions at the local, regional, and national levels due to the negative consequences of increasing pollution and traffic on public health and urban air quality (Rodriguez-Rey et al. 2021 ). Constant exposure to vehicle exhaust fumes can cause lower respiratory tract symptoms such as coughing, dyspnea, and inspiratory pain (Sánchez et al. 2021). Conventional techniques for assessing on-road vehicle emissions frequently depend on periodic vehicle inspections or stationary monitoring stations, both of which have drawbacks including high costs, restricted coverage, and an inability to precisely reflect real-world driving circumstances. Furthermore, real-time data or information on certain vehicle types and emission parameters could not be available using these approaches. One area of concern is the composition of the air quality due to changes in technology and the usage of different fuels. There has long been research on the relationship between death rate and particle air pollution, although many studies may be constrained by the absence of controls for confounding factors(Saini et al. n.d.). Numerous scientific investigations are underway in India as well as other regions of the world. Nearly all major cities, including Lucknow, are polluted, according to studies. Certain criterion pollutants, particularly PM10, exceed the allowed level as outlined in the regulations. There may be a link between vehicle emissions and a rise in respiratory ailments, according to recent research (Singh et al. 2016). The complex blend of chemical agents with varying sizes and forms that make up tiny particulate matter. Research shows that, in addition to PM's quantity and mass concentration, the impact of tiny particulate matter on human health will also rely on its chemical makeup. Both groups have the potential to raise population mortality and morbidity at high levels. It is well known that SO 2 has an impact on both ecosystems and human health. At high concentrations, SO 2 can exacerbate pre-existing heart and lung conditions and cause respiratory illnesses, especially in children and the elderly. Understanding the levels of air quality both today and in the future is crucial for assessment studies. The aforementioned restriction is circumvented by statistical distributional models that are “non-causal” and rely on historical data. and calculate the 'extreme' concentrations with logical precision (Chen et al. 2022 ). Consequently, air pollution causes several issues for health, some of which may be caused by a combination of various pollutants rather than a single pollutant. Particulate matter is one of these pollutants; it is generated by a variety of indoor and outdoor activities and contributes to diseases such as neoplastic, cardiovascular, respiratory, autoimmune, etc. Other pollutants that are harmful to both the environment and human health include NO 2 , which is produced when fuels like diesel and petrol used in cars burn. When these fuels burn, minute particles smaller than 10 micrometers go out into the air, which may significantly affect people (Wang et al. 2017 ). The atmosphere's quality is affected by meteorological factors such as temperature, humidity, wind speed, and sun radiation (Fabregat et al. 2022 ). As particle matter is absorbed by drops of rain, rainfall contributes to a lower pollution level. According to various reports, PM 2.5 pollution is accountable for 6.4 million lives lost and 0.8 million premature deaths worldwide each year. Additionally, magnetic minerals may be present in a sample of particulate matter. About 5 and 15% of urban air PM is composed of iron oxides and hydroxides, which make up the remaining 10 to seventy percent of the bulk iron content. Magnetite was the most common mineral in the particle components (Saini et al. n.d.). The objective of this study is to assess Air Quality, Identification of Pollutants, Quantification of Emissions, and seasonal variation of air pollutants, HYSPLIT4.0 is used to analyse the trajectory frequencies of air mass particles over four seasons. In eastern India, changes in the particulate matter concentration have been found by air trajectories. Rising levels may occur via transportation from origins, as evidenced by findings in Africa, Australia, Asia, the Middle East, North America, South America, and Europe. Table 1 presents the research that is taken to be considered about the Indian environment. (Saini et al. n.d.). Table 1 Studies from many research about the concentration of pollutants Study area Pollutants type Technique used Key observation References 5 Indian rural sites PM 2.5 aerosol optical depth Temporal fluctuation and association Increment of AOD due to urbanization and transportation Gautam at el; 2022 Delhi, Lucknow, Nagpur, Kanpur, Chennai (East India) PM 10 , Black carbon PM 2.5 Back paths with daily fluctuations Black carbon increases with the increases in PM 2.5 concentration Chelani at el; 2022 Karunya Nagar (Tamil Nadu) PM 10 , CO 2 PM 2.5 formaldehyde CHAID decision tree 3D visualization statistical technique Indian cities have higher concentrations than those in other nations because location and metrology have a greater impact on concentration than height above the surface. Gautam at el; 2021 Nagpur PM10, SO 2 , NO 2 Annual variation Due to COVID-19, the concentration of the contaminant was reduced. Saini at el; 2021 Lucknow CO 2 , CO, PM NO 2 , Variation and correlation throughout a year Emission permissible limits Ambade at el; 2021 2. Material and methodology 2.1 Study area Uttar Pradesh's capital and largest megacity is Lucknow, which comes in third behind Delhi and Kolkata. Among the megacities in North, East, and Central India, it comes in third place. Situated around 123 meters above sea level, the area spans 2,528 square km. The humid tropical weather of Lucknow has four distinct seasons: summer, post-monsoon, thunderstorms, and idle time. Major cities including Raebareli, Unnao, Barabanki, Kanpur, Sitapur, and Farrukhabad are located in the vicinity of Lucknow. Seven monitoring stations are located in the megacity by the UPPCB: Hazratganj, Mahanagar, Aliganj, Sarai Mali Khan, Ansal TC, Gomti Nagar, and Talkatora. The largest populated location and main source of pollution is Hazratganj; the Talkatora industrial sector also contributes to air pollution. The study area is shown in the Fig. 1 .According to Road Transport Authority of Lucknow registered Motor Vehicles Non-Transport and Transport Cars city Lucknow unit thousand shown in Fig. 2 . The UPPCB website provided the RSPM, SO 2 , NO 2 , and CO data utilized in this study, while the Indian Meteorological Department provided the meteorological parameters. 2.2 Monitoring Scheme: The CPCB has ordered that the daily average of PM 10 , SO 2 , NO 2 , and CO at all seven monitoring sites be based on the SAMP for monitoring. Control Panel). Gravimetric analysis is used to estimate the particle issue RSPM. For a full day, eight hours a day, at a flow rate of 0.8 to 1.3 m 3 /min, experienced air was run through a fiberglass filter. With its protective enclosure, blower, voltage regulator, time collector, Rota meter, and filter holder, the High-Volume Sampler (HVS) is equipped to remove dust from a 20.3 x 25.4 cm fiberglass filter, which is a cyclone separator that uses centrifugal force (An et al. 2022 ). 2.3 HYSPLIT4.0 : - The National Oceanic and Atmospheric Administration created Hybrid Single Particle Lagrangian Integrated Orbits. It is frequently used to mimic the flow of contaminants like exhaust from moving cars. Researchers may analyze vehicle emissions breakdown patterns using HYSPLIT to learn more about how emissions from vehicles affect the environment, human health, and air quality. Researchers may predict the dispersion of vehicle-emitted pollutants across time and space by gaining access to data on emission rates, vehicle types, weather, and topographical features. Urban planning, public health initiatives, and policymaking that try to lower air pollution all benefit from this knowledge. In this investigation, discrete runs were also conducted using GDAS 1 data (Sánchez et al. 2021b ). With a top swing, this model permits sawing speed. of 0.1 cm/s. at 50 m AGL and bottom swing at 0 m AGL. It assumes a 1-hour unit size oscillation at 12 UTC of the day and an estimated 24-hour back diffusion with a mean period of 6 hours and an average layer above 100 m AGL. In this study, the online version HYSPLIT4.0 was used for trajectory and diffusion studies(Singh et al. 2016). 2.4 Orbit: - Numerous orbital models have been created to study atmospheric motion. They can examine the oscillations of air. This work made use of the NOAA Hybrid Single Particle Lagrangian Integrated Trajectories (HYSPLIT4.0) model. Online at ( http://www.arl.noaa.gov/HYSPLIT_info.php , ) the model is accessible (Goel and Guttikunda 2015 ). The model is used to calculate the initial position of the air returned from the receiving site at the start of the sampling period. Using the HYSPLIT4.0 model, a four-season continuous survey of Lucknow city was conducted to examine aerosol routes from different places up to 500 meters above ground level (Kovács et al. 2021b ). Long-range transmission of aerosols from various sources over 6 hours was studied. 48-hour mass isentropic back trajectories in Lucknow were computed in the investigation of long-term transport of aerosols from various places, at 6-hour intervals (Sadiq et al. 2016 ). The RSPM allowable value recommended by the Central Pollution Control Board is 100 (µg-m − 3 ). Table 2 Pollutant yearly Mean Concentration (in µg m − 3 ) PM 10 , SO 2 , NO 2 , and CO at all seven monitoring Stations (i.e. Industrial, Residential, Commercial, area). STATION CONCENTRATION (µg m − 3 ) (ANNUAL AVERAGE) YEARS 2016 2017 2018 2019 2020 2021 2022 HAZRATGANJ (COMMERCIAL) PM 10 217.68 323.05 244.95 197.76 211.21 216.45 226.45 SO 2 7.77 8.33 8.39 8.61 7.77 7.47 7.67 NO 2 26.91 26.81 30.16 30.95 33.42 31.32 32.32 CO 28.12 29.22 29.38 29.56 30.09 29.69 30.69 SARAI MALI KHAN (COMMERCIAL) PM 10 216.33 243.25 230.86 216.38 163.56 187.56 197.56 SO 2 7.98 8.70 8.53 8.94 7.55 7.88 7.78 NO 2 27.50 26.28 29.30 31.86 31.15 31.59 31.89 CO 27.67 27.89 28.09 28.88 30.87 31.67 31.87 ANSAL T.C. (COMMERCIAL) PM 10 204.13 197.09 208.84 201.09 166.61 177.61 187.61 SO 2 7.24 7.27 7.63 8.40 7.11 7.98 7.17 NO 2 26.05 23.52 26.62 28.92 30.73 32.53 32.73 CO 28.34 28.76 28.99 29 28.67 32.67 33.67 GOMTI NAGAR (COMMERCIAL) PM 10 214.50 233.55 218.22 190.02 158.96 182.96 188.96 SO 2 7.42 8.11 8.15 8.14 7.51 8.31 8.51 NO 2 26.82 25.22 28.27 29.59 30.68 30.68 29.68 CO 28.76 29.07 29.56 28.67 28.45 31.45 30.45 MAHANAGAR (RESIDENTIAL) PM 10 198.13 212.06 204.50 169.28 189.26 189.26 199.26 SO 2 7.54 8.24 7.88 7.84 7.58 7.18 7.58 NO 2 26.91 25.26 28.87 29.70 31.61 29.61 30.61 CO 27.67 27.87 28.65 28.66 28.89 32.89 33.89 ALIGANJ (RESIDENTIAL) PM 10 211.87 199.28 174.73 174.75 148.74 168.74 178.74 SO 2 7.48 7.48 7.26 7.63 7.22 7.62 7.82 NO 2 26.84 23.92 26.06 30.32 31.16 31.36 31.96 CO 29.23 29.45 28.34 29.92 30.43 30.53 30.93 TALKATORA (INDUSTRIAL) PM 10 216.59 214.07 229.43 224.60 221.73 269.73 299.73 SO 2 7.92 8.85 7.96 8.62 7.95 8.28 8.88 NO 2 27.38 27.04 30.59 31.77 33.46 32.76 33.86 CO 30.34 29.56 30.45 28.67 29.56 31.56 33.23 Table 2 shows annual averages concentration of air pollutants in different areas, such as commercial, residential, industrial over a study period. An air pollution's annual average concentration is the mean concentration of different dangerous compounds in the atmosphere over a specific period of time, usually a year. Particulate matter, carbon monoxide, nitrogen dioxide, and sulphur dioxide represent a few types of these contaminants. Monitoring air quality and any potential risks to people depends on the monitoring and reporting of these concentrations. In order to safeguard the environment and public health, regulatory authorities and environmental agencies frequently develop standards or guidelines for acceptable concentrations of particular contaminants. The actual average concentrations may vary significantly based on a number of variables, including geographic features, traffic density, industrial activity, location (rural vs. urban), and weather patterns. For the purpose of to track these concentrations and give information to the public, academics, and policymakers, monitoring stations and air quality indices are helpful (P. G. Kumar et al. 2020 ). 3. Results and discussion In the following sections, average concentration variations with PM 10 , SO 2 , NO 2 , and CO has been evaluated and discussed: 3.1 Annual average concentration Fig.3(a) displays the yearly average PM 10 concentration over Lucknow City. At every monitoring site between 2016 and 2022, there is a significant amount of fluctuation. Hazratganj (323.05 µg-m -3 ) station had the highest yearly average concentration of any station in 2017 because of its high population density and substantial traffic problems. Hazratganj is a business neighborhood. Conversely, Ansal TC displayed varying attention during the study period, presumably as a result of the institution's annual holidays on certain days. Due to the Covid lockdown, several anthropogenic activities were shut down in 2020. As a result, Sarai Mali Khan and Gomti Nagar showed a gradual increase until 2017, with a peak concentration of 243.25 and 233.55 µg m -3 in 2017, respectively, and a variation of 163.56 and 158.96 µg m -3 respectively till 2022. In 2020, the residential areas of Aliganj and Mahanagar exhibited a minimum concentration of 118.34 and 153.52µg-m -3 , respectively. Due to the COVID-19 pandemic-related lockdown, there were also fewer anthropogenic activities and vehicle movements in the region, which contributed to the fall in concentrations. Throughout the investigation, it was found that the Talkatora and Sarai Mali Khan stations had the highest yearly average concentration of SO 2 [Fig.3(b)]. It may be the result of the area's heavy usage of fossil fuels, such as coal and oil. The industrial Talkatora station has the highest concentration in 2022 8.88 µg m -3 , while the commercial Ansal TC station displays the lowest concentration 8.40 µg m -3 in 2019 due to the closure of the institutional sector during the Covid shutdown. Because they have the highest population density relative to other commercial areas, residential neighbourhoods like Mahanagar and Aliganj also exhibit a notable concentration of SO 2 . [Fig.3(c)] illustrates the annual fluctuation of NO 2 , which abruptly decreased in 2019 as a result of the COVID-19 lockdown and continued to rise until 2022. The stations in Talkatora (an industrial region) and Hazratganj (a commercial district) have the highest concentrations due to the highest levels of traffic congestion, whereas Aliganj displays the lowest concentration surge ( CATALOGUE INDIAN EMISSION INVENTORY REPORTS 2022). The commercial areas of Ansal TC, Gomti Nagar, and Sarai Mali Khan exhibit an average increase in NO 2 concentration. Annual variation of CO shows [Fig.3(d)] Talkatora (industrial), Hazratganj (commercial) and Ansal TC, (commercial) stations account for maximum concentrations of 33.67,30.69,31.87 µg m -3 respectively. While Aliganj and Mahanagar, which are mainly residential areas, see only a small leap in concentration, those regions have the highest levels of traffic congestion (Huangfu and Atkinson 2020). The commercial zones, Sarai Mali Khan, and Gomti Nagar show an average increase in CO concentration. 3.2 Seasonal variation At each of the seven monitoring stations, the seasonal fluctuation in the pollutants' concentrations has been examined. Winter (December to February) and Summer are the seasons taken into consideration (March to June), post-monsoon (October to November), and Monsoon (June to September). 3.2.1 Variation in PM 10 – Fig.4 shows the seasonal variation of PM 10 concentration (µg m -3 ) and shows discernible changes during the research period. The post-monsoon season at Hazratganj station Fig.4(a) has the greatest PM 10 concentration, with winter, summer, and monsoon following suit. The CPCB threshold of 100 µg-m -3 was exceeded by the maximum concentration of 447.46 µg m -3 for the post-monsoon of 2017 and the minimum concentration of 114.30 µg m -3 for the monsoon of 2016 (Chaurasia et al. 2018). Similar to this, the post-monsoon and winter values at Sarai Mali Khan, Gomti Nagar, and Talkatora [Fig.4(b), Fig.4(d), and Fig.4(g)] are nearly higher than those of the other two seasons. Elevated measurements during the post-monsoon and winter seasons may be attributed to the existence of locally generated and transported contaminants, in addition to the maintenance of stable conditions throughout the area during that period. Furthermore, a region-wide temperature inversion may occur, intensifying the pollutants and increasing their concentration over the research area. During the monsoon season, rainfall lowers the concentration, allowing pollutants to disperse throughout the region. 3.2.2 SO 2 variation The concentration is below the 80µg m -3 recommended limits set by the Central Pollution Control Board, as indicated by the horizontal lines in Fig.5 and the seasonal variation of SO 2 depicted. The concentrations at Stations Hazratganj [Fig.5(a)] and Sarai Mali Khan [Fig.5(b)] during the post-monsoon season in 2017 peaked at 10.92 µg m -3 and 10.97 µg-m -3 , respectively. On the other hand, Gomti Nagar [Fig.5(d)] displays the highest concentration during the winter of 2016 of 10.09 µg m -3 , whereas Ansal TC [Fig.5(c)] has a highest concentration of 11.40 µg m -3 during the post-monsoon of 2019. In the winter of 2016, the maximum SO 2 concentrations at Mahanagar [Fig.5(e)] and Aliganj [Fig.5(f)] were 10.09 µg m -3 and 10.84 µg m -3 , respectively, however they were lower. focus on other stations. In the post-monsoon season of 2017, Talkatora [Fig.5(g)] had a maximum of 11.06 µg m -3 . The usage of biomass as a residential fuel in cities may be the source of the elevated SO 2 concentrations observed in the winter and post-monsoon seasons. Significantly more SO 2 is produced at night as a result of the city burning biomass to fight the bitter cold. Similar to the commercial sector, the SO 2 content is higher there, most likely as a result of excessive biomass burning. 3.2.3 NO 2 variation The seasonal change in NO 2 concentration is seen in Fig.6. The NO 2 concentrations at Talkatora and Hazratganj and [Fig.6(g), Fig.6(a)] show a progressive increase during almost every season, peaking at 38.40 and 37.20 µg m -3 in the post-monsoon of 2022. Sarai Mali Khan and Ansal TC [Fig.6(b), Fig.6(c)] show almost the same variation throughout the study; their highest concentrations were 35.00 µg m -3 during the winter of 2019 and 34.40 µg m -3 during the post-monsoon of 2020, respectively. The summer of 2019 is when Gomti Nagar [Fig.6(d)] reaches its highest concentration of 33.33 µg m -3 . Peak concentrations were recorded in Mahanagar and Aliganj [Fig.6(e), Fig.6(f)] during the post-monsoon of 2020 and the winter of 2019, respectively, at 34.90 and 35.40 µg m -3 . In comparison to other commercial and residential monitoring locations, there might be less vehicle traffic on this industrial site. 3.2.4 CO variation shows how the concentration of CO varies with the season. The CO concentrations in Hazratganj and Talkatora [Fig. 7(a), Fig.7(g)] show a progressive increase across almost every season, peaking at 37.71 and 37.0 µg m -3 during the post-monsoon in 2022. Ansal TC and Sarai Mali Khan [Fig.7(c), Fig.7(b)] show almost the same variation throughout the study; their peak concentrations were 37.67 during the winter of 2022 and 36.30 µg-m -3 during the post-monsoon of 2022, respectively. Gomti Nagar [Fig.7(d)] gets its peak concentration of 34.45 µg m -3 in the summer of 2017. Peak concentrations were measured at 36.03 and 36.00 µg m -3 in Mahanagar and Aliganj [Fig.7(e), Fig.7(f)] in the winter of 2022 and the post-monsoon of 2022, respectively. In comparison to other commercial and residential monitoring locations, there might be higher vehicle traffic on this industrial site. 3.3 Area-wise variation Fig.8 shows the area-wise fluctuation of PM 10 , SO 2 , NO 2 , and CO concentrations for all monitoring stations. When it comes to PM 10 concentration at commercial stations [Fig. 8(a)], Hazratganj is in the lead among other stations, having peaked in 2017 at 323.05 µg m -3 . Other stations that follow include Gomti Nagar, Ansal TC, and Sarai Mali Khan. After 2017, Talkatora, an industrial station, had the highest concentration of PM 10 of any place. This could be attributed to an increase in industrial activity in Lucknow city's surrounding districts. In 2018, Talkatora recorded a maximum concentration of 229.43 µg m -3 , whereas Aliganj and Mahanagar had minimal concentrations of 174.72 and 174.75 µg m -3 , respectively, in 2019 and 2018. Sarai Mali Khan has the highest SO 2 concentration [Fig.8(b)], at 8.94 µg m -3 , followed by Hazratganj, Gomti Nagar, and Ansal TC, which is for commercial zones. Nonetheless, compared to the other locations, the industrial zones had a higher concentration. The industrial region of Talkatora has the highest concentration 9.13 µg m -3 , followed by the residential areas of Mahanagar and Aliganj. Hazratganj and Sarai Mali Khan exhibit competition for variance in NO 2 concentration [Fig.8(c)], with the former having the highest concentration of 33.01 µg m -3 in 2022 and the latter having the highest concentration of 31.86 µg m -3 in 2019. Hazratganj and Talkatora indicate competition for variation in CO concentration [Fig.8(d)], with the former having a highest concentration of 31.00 µg m -3 in 2021 and the latter having a maximum concentration of 32 µg m -3 in 2022. 3.4 Dispersion Model The dispersion of pollutants in Lucknow city has been studied using the HYSPLIT4.0 dispersion modelling. Pollution dispersion is used in HYSPLIT4.0 dispersion modelling for the winter, summer, monsoon, and post-monsoon seasons. The online version of HYSPLIT4.0 employs Backward dispersion for 24 hours, with average intervals of 6 hours each, to disperse pollutants. figures Fig.9(a) for winter, Fig.9(b) for summer, Fig.9(c) for monsoon, and Fig.9(d) for post-monsoon display all of the results. The star emblem, which has four concentric rings spaced 50 kilometres apart, represents the city of Lucknow. Fig 9(a) for winter, Fig.9(b) for summer, Fig. 9(c) for monsoon, and Fig. 9(d) for post-monsoon display all of the results. The star emblem, which has four concentric rings spaced 50 kilometres apart, represents the city of Lucknow. The findings show Fig.10 that during the winter and post-monsoon seasons, the plumes are advocating from the northwest, while during the monsoon and summer seasons, they are advocating from the east-south. 4. Conclusion The analysis of changes in the RSPM, SO 2 , NO 2 , and CO yearly average concentrations in Lucknow city between 2016 and 2022 was presented in this work. For this study, Hazratganj, Aliganj, Talkatora, Sarai Mali Khan, Gomti Nagar, Mahanagar, and Ansal TC were taken into consideration. It has been investigated how the concentrations of all four pollutants—RSPM, SO 2 , NO 2 , and CO vary annually, seasonally, and area-wise. At each of the seven monitoring stations, the annual average concentration of RSPM ranges from 148.74 to 323.05 µg-m − 3 , SO 2 from 7.11 to 8.94 µg-m − 3 , NO 2 from 23.52 to 31.86 µg-m − 3 , and CO from 27.38 µg-m − 3 to 33.89 µg/m 3 . It was found that the RSPM and CO concentrations were above the CPCB-recommended limit. Conversely, it was found that the SO 2 and NO 2 concentrations were below the recommended levels. Based on a comparison of seasonal variations, the highest recorded RSPM concentration during the post-monsoon season falls between 160.15 and 447.47 µg-m − 3 . Due to the rain at every monitoring site, the concentration is lower during the monsoon season than it is during the other seasons. For every season, the range of SO 2 , NO 2 , and CO concentrations was recorded to be 5.55–10.94 µg-m − 3 , 20.23–38.40 µg-m − 3 , and 27.38–33.89 µg-m − 3 , respectively. According to area-wise concentration, industrial locations have higher levels of pollution than commercial and residential areas. According to the statistics gathered, Lucknow city's pollution level for 2022 will significantly fall as a result of the lockdown brought on by the COVID-19 epidemic. Even during the COVID-19 Lockdown, the study period's annual average concentration of RSPM was found to be high at all stations and over the recommended level of 100 µg-m − 3 . Based on trajectory analysis, winds that blow from a north-westerly direction all year round are mostly responsible for the migration of aerosols from developed areas and cities. The present study shows that Lucknow's air quality is negatively impacted by pollution, which is not only generated locally but also mostly transported from nearby industrial and urbanized regions. This might be the reason for the declining air quality in the city despite measures taken by the Indian government or the local municipal authority. The dispersion analysis carried out with HYSPLIT4.0 for examples chosen based on the highest concentration for various seasons in the city across the research period has likewise supported this result. The RSPM time-series data indicates the presence of both yearly and semi-annual periodicity throughout the research period. However, in addition to theoretical and experimental understanding of source allocation studies and pollutant discharge across the region, more data collection and analysis in the Lucknow metropolitan area is required. When compared to SO 2 and NO 2 pollutants, the results show that lowering particulate matter and carbon monoxide pollution in the city is a serious problem and is at a critical stage. Abbreviations RSPM - Respirable Suspended Particulate Matter PM 10 - Particulate matter particle size less than 10 mm SO 2 - Sulphur dioxide NO 2 - Nitrogen dioxide CPCB - Central Pollution Control board NCEP - National Centres for Environment Prediction GDAS - Global Data Assimilation System AGL - Above Ground Level NAMP - National Ambient Monitoring Programme HYSPLIT4.0 - Hybrid Single-Particle Lagrangian Integrated Trajectory model version 4 NOAA - National Oceanic and Atmospheric Administration NCAR - National Centre for Atmospheric Research RDS - Respirable dust sampler SAMP – State Ambient Monitoring Programme UPPCB – Uttar Pradesh Pollution Control Board Declarations Acknowledgment The authors would like to extend their gratefulness to the Civil Engineering Department, Institute of Engineering and Technology Lucknow for providing the guidance and support that are required to conduct the study, Uttar Pradesh Pollution Control Board (UPPCB), Central Pollution Control Board (CPCB), and New Delhi for supporting the projects under Programs for State and National Air Quality Monitoring (SAMP) and NAMP (National Air Quality Monitoring). Authors Contributions Vipin Kumar: Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing, Validation, Software investigation. Prabhat Kumar Patel: Supervision, Validation, Visualization, Writing – review & editing, Conceptualization. Conflicting Interests: All contributors declare that no relationships of interest occur. Role of Funding Sources: This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors. Ethical Statement: The author disclosed that there is no human or animal heart in this study. References An, F., Liu, J., Lu, W., & Jareemit, D. (2022). Comparison of exposure to traffic-related pollutants on different commuting routes to a primary school in Jinan, China. Environmental Science and Pollution Research , 29 (28), 43319–43340. https://doi.org/10.1007/s11356-021-18362-w Biramo, Z. B., & Mekonnen, A. A. (2022). Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track. Environmental Systems Research , 11 (1). https://doi.org/10.1186/s40068-022-00276-2 CATALOGUE INDIAN EMISSION INVENTORY REPORTS . (2022). Chaurasia, S., Ahmad, I., & Kumar Pandey, R. (2018). Issue 12 www.jetir.org (ISSN-2349-5162) . JETIR1812C01 Journal of Emerging Technologies and Innovative Research (Vol. 5). www.jetir.org Chen, Z., Zan, Z., & Jia, S. (2022). Effect of urban traffic-restriction policy on improving air quality based on system dynamics and a non-homogeneous discrete grey model. Clean Technologies and Environmental Policy , 24 (8), 2365–2384. https://doi.org/10.1007/s10098-022-02319-9 Fabregat, A., Vernet, A., Vernet, M., Vázquez, L., & Ferré, J. A. (2022). Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality. 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Batch adsorption characteristics of Dowex Marathon MSA commercial resin for Au(III) removal from synthetic electroless plating solutions. Materials Today: Proceedings , 68 , 824–829. https://doi.org/10.1016/j.matpr.2022.06.258 Patel, P. K., Pandey, L. M., & Uppaluri, R. V. S. (2023a). Synthesized carboxymethyl-chitosan variant composites for cyclic adsorption- desorption based removal of Fe, Pb, and Cu. Chemosphere , 139780. https://doi.org/https://doi.org/10.1016/j.chemosphere.2023.139780 Patel, P. K., Pandey, L. M., & Uppaluri, R. V. S. (2023b). Adsorptive removal of Zn, Fe, and Pb from Zn dominant simulated industrial wastewater solution using polyvinyl alcohol grafted chitosan variant resins. Chemical Engineering Journal , 459 (January), 141563. https://doi.org/10.1016/j.cej.2023.141563 Patel, P. K., Pandey, L. M., & Uppaluri, R. V. S. (2024). Highly effective removal of multi-heavy metals from simulated industrial effluent through an adsorption process employing carboxymethyl-chitosan composites. Environmental Research , 240 . https://doi.org/10.1016/j.envres.2023.117502 Rodriguez-Rey, D., Guevara, M., Linares, M. P., Casanovas, J., Salmerón, J., Soret, A., et al. (2021). A coupled macroscopic traffic and pollutant emission modelling system for Barcelona. Transportation Research Part D: Transport and Environment , 92 . https://doi.org/10.1016/j.trd.2021.102725 Sadiq, A., El Fazziki, A., Ouarzazi, J., & Sadgal, M. (2016). Towards an agent based traffic regulation and recommendation system for the on-road air quality control. SpringerPlus , 5 (1). https://doi.org/10.1186/s40064-016-3282-2 Saini, D., Mishra, N., & Lataye, D. H. (n.d.). Variation of ambient air pollutants concentration over Lucknow city, trajectories and dispersion analysis using HYSPLIT4.0. https://doi.org/10.1007/s12046-022-02001-2S Sánchez, J. M., Ortega, E., López-Lambas, M. E., & Martín, B. (2021a). Evaluation of emissions in traffic reduction and pedestrianization scenarios in Madrid. Transportation Research Part D: Transport and Environment , 100 . https://doi.org/10.1016/j.trd.2021.103064 Sánchez, J. M., Ortega, E., López-Lambas, M. E., & Martín, B. (2021b). Evaluation of emissions in traffic reduction and pedestrianization scenarios in Madrid. Transportation Research Part D: Transport and Environment , 100 . https://doi.org/10.1016/j.trd.2021.103064 Singh, D., Shukla, S. P., Sharma, M., Behera, S. N., Mohan, D., Singh, N. B., & Pandey, G. (2016a). GIS-Based On-Road Vehicular Emission Inventory for Lucknow, India. Journal of Hazardous, Toxic, and Radioactive Waste , 20 (4). https://doi.org/10.1061/(asce)hz.2153-5515.0000244 Singh, D., Shukla, S. P., Sharma, M., Behera, S. N., Mohan, D., Singh, N. B., & Pandey, G. (2016b). GIS-Based On-Road Vehicular Emission Inventory for Lucknow, India. Journal of Hazardous, Toxic, and Radioactive Waste , 20 (4). https://doi.org/10.1061/(asce)hz.2153-5515.0000244 Wang, Y., Liang, X., Wang, Y., & Yu, H. (2017). Effects of Viscosity Index Improver on Morphology and Graphitization Degree of Diesel Particulate Matter. In Energy Procedia (Vol. 105, pp. 4236–4241). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.03.910 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4295589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":326645778,"identity":"69077dfa-32b0-4e06-9c95-0d6b007bec57","order_by":0,"name":"Vipin Kumar","email":"","orcid":"","institution":"Institute of Engineering and Technology Lucknow","correspondingAuthor":false,"prefix":"","firstName":"Vipin","middleName":"","lastName":"Kumar","suffix":""},{"id":326645779,"identity":"42ad1877-70d5-4c9c-94ca-d1aebe9db6ff","order_by":1,"name":"Prabhat Kumar Patel","email":"data:image/png;base64,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","orcid":"","institution":"Institute of Engineering and Technology Lucknow","correspondingAuthor":true,"prefix":"","firstName":"Prabhat","middleName":"Kumar","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2024-04-20 03:09:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4295589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4295589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60390480,"identity":"c36b2610-082a-468b-9f2b-93b028c5bb69","added_by":"auto","created_at":"2024-07-16 08:54:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167899,"visible":true,"origin":"","legend":"\u003cp\u003eLucknow city study area map with all seven monitoring locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/aad6ba548bf3672ea3564951.png"},{"id":60391771,"identity":"3f210ac7-bfcf-4b65-a064-55cac05eb40f","added_by":"auto","created_at":"2024-07-16 09:10:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32034,"visible":true,"origin":"","legend":"\u003cp\u003eRegistered Motor Vehicles Non-Transport and Transport Cars city Lucknow unit thousand\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/291e74a9779606f2c5d93ae2.png"},{"id":60390481,"identity":"95fbf6cd-efa6-4ad7-9309-3c67991a14a9","added_by":"auto","created_at":"2024-07-16 08:54:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31126,"visible":true,"origin":"","legend":"\u003cp\u003ePollutant yearly Mean Concentration (in µg m\u003csup\u003e-3\u003c/sup\u003e) (a) PM\u003csub\u003e10\u003c/sub\u003e, (b) SO\u003csub\u003e2 \u003c/sub\u003e(c) NO\u003csub\u003e2\u003c/sub\u003e, and (d) CO Over the research period of 2016–2022 at all monitoring stations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/58e753eb1c943cd64b600b69.png"},{"id":60391218,"identity":"f7b7f59e-2246-47cf-9e50-977b65a75075","added_by":"auto","created_at":"2024-07-16 09:02:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84363,"visible":true,"origin":"","legend":"\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e seasonal variation graphs (, monsoon, and post-monsoon, winter, summer) for the study period of 2016–2022 at all monitoring stations.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/17be5e80249e3b395a52541b.png"},{"id":60390488,"identity":"1fe13bba-6e83-4c3d-9f99-482000014f30","added_by":"auto","created_at":"2024-07-16 08:54:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35792,"visible":true,"origin":"","legend":"\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e seasonal variation graphs (monsoon, and post-monsoon, winter, summer,) for the study period of 2016–2022 at all monitoring stations.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/f2cfb143c5d11213ef21c58f.png"},{"id":60390485,"identity":"d5a06a23-ec35-4454-afd8-f43996872b01","added_by":"auto","created_at":"2024-07-16 08:54:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":87895,"visible":true,"origin":"","legend":"\u003cp\u003eNO\u003csub\u003e2 \u003c/sub\u003eseasonal variation graphs (monsoon, and post-monsoon, winter, summer) for the study period of 2016–2022 at all monitoring stations.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/85cd28abb6c5c22ab70e867a.png"},{"id":60390484,"identity":"ac7185a7-06fd-497e-9474-8e6d9f95bc87","added_by":"auto","created_at":"2024-07-16 08:54:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":89274,"visible":true,"origin":"","legend":"\u003cp\u003eCO seasonal variation graphs (monsoon, and post-monsoon, winter, summer,) for the study period of 2016–2022 at all monitoring stations.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/ce67c4b9333aa62f1f34c543.png"},{"id":60390490,"identity":"495c852b-5802-4c1c-a37e-578d141f6edc","added_by":"auto","created_at":"2024-07-16 08:54:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":49818,"visible":true,"origin":"","legend":"\u003cp\u003eDuring the study period of 2016–2022, an area-wise annual average comparison was conducted between commercial stations, industrial stations, and residential stations for (a) PM\u003csub\u003e10\u003c/sub\u003e, (b) SO\u003csub\u003e2\u003c/sub\u003e, (c) NO\u003csub\u003e2\u003c/sub\u003e, and (d) CO\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/fba5f334eefa74e125ff5e4e.png"},{"id":60391220,"identity":"9fa3df0c-c3b1-4718-8eac-d858dc6def11","added_by":"auto","created_at":"2024-07-16 09:02:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1345551,"visible":true,"origin":"","legend":"\u003cp\u003eThe air mass particle trajectory frequencies throughout the (a) monsoon, (b) post-monsoon, (c) winter, and (d) summer are analyzed using HYSPLIT4.0. The 48-hour isentropic back orbits with a 500 m AGL were applied every six hours, or 00, 06, 12, and 18 UTC, every day. The star sign indicates the location of the city.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/59abf3cc2498fbb7a8747aba.png"},{"id":60391221,"identity":"c5a6eae0-6502-4f09-b537-714796135172","added_by":"auto","created_at":"2024-07-16 09:02:48","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":459151,"visible":true,"origin":"","legend":"\u003cp\u003eDispersion map using HYSPLIT4.0\u0026nbsp; \u0026nbsp;to forecast the concentration of air pollutants such as PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO in all monitoring stations.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/e8b2678a3c64799e623ec9db.png"},{"id":60392688,"identity":"9a2f28ad-243c-4671-a49f-4e787a6ffccc","added_by":"auto","created_at":"2024-07-16 09:18:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2980587,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4295589/v1/efd64c40-2939-4f76-9dd6-de2cea808392.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Change in the concentration of pollutants in the air over the city of Lucknow, together with HYSPLIT4.0's trajectory and dispersion analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEverything that exists on Earth depends on water and air which are also a vital resource for maintaining ecosystems, human health, and economic growth in appropriate amounts and of high quality (Patel et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). But the world's population is growing, urbanization is happening, and farming methods are changing. As a result, air pollution and wastewater output are rising significantly, endangering human health and the environment (P. Kumar et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Patel et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Either correctly handled or improperly treated wastewater can damage aquatic environments, taint streams, and spread illnesses that are spread by water. In response to these problems, wastewater treatment has emerged as a crucial element of environmental operations, helping to reduce the adverse effects of releases of wastewater on ecosystems and human health.\u003c/p\u003e \u003cp\u003eAir pollution is a severe problem with the environment that has an impact on people's quality of life, the state of the environment, and overall wellness. One of the primary causes of air pollution is vehicle emissions, particularly in cities where traffic congestion is a daily occurrence. When fossil fuels are used in automobiles, a complex cocktail of pollutants is released, such as particulates (PM), nitrogen oxides (NOx), sulfur oxides (SOx), the gas carbon monoxide (CO), and other hazardous pollutants. These substances cause major threats to both human health and the environment.\u003c/p\u003e \u003cp\u003eThe capital of Uttar Pradesh is Lucknow Megacity, which has a population of 2.82\u0026nbsp;million according to 2011 data. Its area is 310 km\u003csup\u003e2\u003c/sup\u003e, and its coordinates are 26\u0026deg; 52' N latitude to 80 \u0026deg; 56' E longitude, or 128 cadence above the ocean. Urban development has made the transport sector the focus of attention over the past 10 years, along with energy. In India, the transport industry uses around 16.9% of the country's total energy or 36.5\u0026nbsp;million tonnes of oil equivalent. Two-wheelers have seen a remarkable increase in proportion across all motor vehicle categories; they currently make up 70% of all motor vehicles in India (Ilarri et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main source of particulate air pollution in Lucknow is vehicle emissions. As of March 31, 2020, there were 34,24,478 car orders registered with the Regional Transport Office (RTO) in Lucknow, which represents a 13.35 gain over the previous period. On some of Lucknow City\u0026rsquo;s roads, the Uttar Pradesh State Road Transport Corporation (UPSRTC) launched machine services under the name \u0026ldquo;Lucknow Mahanagar Parivahan Sewa.\u0026rdquo; Given that air pollution in urban areas is mostly caused by motor vehicle emissions (traffic) (Biramo and Mekonnen \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kov\u0026aacute;cs et al. 2021). There is a major route close to Hazarat Ganj, Mahanagar, Talkatora, Aliganj, Sarai Malikhan, ATC, and Gomti Nagar that sees a lot more traffic than other roads do year-round. A regulatory framework is required for the management of traffic, air quality, and emissions at the local, regional, and national levels due to the negative consequences of increasing pollution and traffic on public health and urban air quality (Rodriguez-Rey et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Constant exposure to vehicle exhaust fumes can cause lower respiratory tract symptoms such as coughing, dyspnea, and inspiratory pain (S\u0026aacute;nchez et al. 2021).\u003c/p\u003e \u003cp\u003eConventional techniques for assessing on-road vehicle emissions frequently depend on periodic vehicle inspections or stationary monitoring stations, both of which have drawbacks including high costs, restricted coverage, and an inability to precisely reflect real-world driving circumstances. Furthermore, real-time data or information on certain vehicle types and emission parameters could not be available using these approaches. One area of concern is the composition of the air quality due to changes in technology and the usage of different fuels. There has long been research on the relationship between death rate and particle air pollution, although many studies may be constrained by the absence of controls for confounding factors(Saini et al. n.d.). Numerous scientific investigations are underway in India as well as other regions of the world. Nearly all major cities, including Lucknow, are polluted, according to studies. Certain criterion pollutants, particularly PM10, exceed the allowed level as outlined in the regulations. There may be a link between vehicle emissions and a rise in respiratory ailments, according to recent research (Singh et al. 2016).\u003c/p\u003e \u003cp\u003eThe complex blend of chemical agents with varying sizes and forms that make up tiny particulate matter. Research shows that, in addition to PM's quantity and mass concentration, the impact of tiny particulate matter on human health will also rely on its chemical makeup. Both groups have the potential to raise population mortality and morbidity at high levels. It is well known that SO\u003csub\u003e2\u003c/sub\u003e has an impact on both ecosystems and human health. At high concentrations, SO\u003csub\u003e2\u003c/sub\u003e can exacerbate pre-existing heart and lung conditions and cause respiratory illnesses, especially in children and the elderly. Understanding the levels of air quality both today and in the future is crucial for assessment studies. The aforementioned restriction is circumvented by statistical distributional models that are \u0026ldquo;non-causal\u0026rdquo; and rely on historical data. and calculate the 'extreme' concentrations with logical precision (Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequently, air pollution causes several issues for health, some of which may be caused by a combination of various pollutants rather than a single pollutant. Particulate matter is one of these pollutants; it is generated by a variety of indoor and outdoor activities and contributes to diseases such as neoplastic, cardiovascular, respiratory, autoimmune, etc. Other pollutants that are harmful to both the environment and human health include NO\u003csub\u003e2\u003c/sub\u003e, which is produced when fuels like diesel and petrol used in cars burn. When these fuels burn, minute particles smaller than 10 micrometers go out into the air, which may significantly affect people (Wang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The atmosphere's quality is affected by meteorological factors such as temperature, humidity, wind speed, and sun radiation (Fabregat et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs particle matter is absorbed by drops of rain, rainfall contributes to a lower pollution level. According to various reports, PM\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e pollution is accountable for 6.4\u0026nbsp;million lives lost and 0.8\u0026nbsp;million premature deaths worldwide each year. Additionally, magnetic minerals may be present in a sample of particulate matter. About 5 and 15% of urban air PM is composed of iron oxides and hydroxides, which make up the remaining 10 to seventy percent of the bulk iron content. Magnetite was the most common mineral in the particle components (Saini et al. n.d.).\u003c/p\u003e \u003cp\u003eThe objective of this study is to assess Air Quality, Identification of Pollutants, Quantification of Emissions, and seasonal variation of air pollutants, HYSPLIT4.0 is used to analyse the trajectory frequencies of air mass particles over four seasons.\u003c/p\u003e \u003cp\u003eIn eastern India, changes in the particulate matter concentration have been found by air trajectories. Rising levels may occur via transportation from origins, as evidenced by findings in Africa, Australia, Asia, the Middle East, North America, South America, and Europe. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the research that is taken to be considered about the Indian environment. (Saini et al. n.d.).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudies from many research about the concentration of pollutants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollutants type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTechnique used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey observation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 Indian rural sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e aerosol optical depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemporal fluctuation and association\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncrement of AOD due to urbanization and transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGautam at el;\u003c/p\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelhi, Lucknow, Nagpur, Kanpur, Chennai (East India)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e, Black carbon PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBack paths with daily fluctuations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack carbon increases with the increases in PM\u003csub\u003e2.5\u003c/sub\u003e concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChelani at el;\u003c/p\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarunya Nagar (Tamil Nadu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eformaldehyde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHAID decision tree 3D visualization statistical technique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndian cities have higher concentrations than those in other nations because location and metrology have a greater impact on concentration than height above the surface.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGautam at el;\u003c/p\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNagpur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM10, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDue to COVID-19, the concentration of the contaminant was reduced.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaini at el;\u003c/p\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLucknow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e, CO, PM NO\u003csub\u003e2\u003c/sub\u003e,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariation and correlation throughout a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmission permissible limits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAmbade at el;\u003c/p\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"2. Material and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eUttar Pradesh's capital and largest megacity is Lucknow, which comes in third behind Delhi and Kolkata. Among the megacities in North, East, and Central India, it comes in third place. Situated around 123 meters above sea level, the area spans 2,528 square km. The humid tropical weather of Lucknow has four distinct seasons: summer, post-monsoon, thunderstorms, and idle time. Major cities including Raebareli, Unnao, Barabanki, Kanpur, Sitapur, and Farrukhabad are located in the vicinity of Lucknow. Seven monitoring stations are located in the megacity by the UPPCB: Hazratganj, Mahanagar, Aliganj, Sarai Mali Khan, Ansal TC, Gomti Nagar, and Talkatora. The largest populated location and main source of pollution is Hazratganj; the Talkatora industrial sector also contributes to air pollution. The study area is shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.According to Road Transport Authority of Lucknow registered Motor Vehicles Non-Transport and Transport Cars city Lucknow unit thousand shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe UPPCB website provided the RSPM, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO data utilized in this study, while the Indian Meteorological Department provided the meteorological parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Monitoring Scheme:\u003c/h2\u003e \u003cp\u003eThe CPCB has ordered that the daily average of PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO at all seven monitoring sites be based on the SAMP for monitoring. Control Panel). Gravimetric analysis is used to estimate the particle issue RSPM. For a full day, eight hours a day, at a flow rate of 0.8 to 1.3 m\u003csup\u003e3\u003c/sup\u003e/min, experienced air was run through a fiberglass filter. With its protective enclosure, blower, voltage regulator, time collector, Rota meter, and filter holder, the High-Volume Sampler (HVS) is equipped to remove dust from a 20.3 x 25.4 cm fiberglass filter, which is a cyclone separator that uses centrifugal force (An et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 HYSPLIT4.0\u003c/b\u003e: -\u003c/h2\u003e \u003cp\u003eThe National Oceanic and Atmospheric Administration created Hybrid Single Particle Lagrangian Integrated Orbits. It is frequently used to mimic the flow of contaminants like exhaust from moving cars. Researchers may analyze vehicle emissions breakdown patterns using HYSPLIT to learn more about how emissions from vehicles affect the environment, human health, and air quality. Researchers may predict the dispersion of vehicle-emitted pollutants across time and space by gaining access to data on emission rates, vehicle types, weather, and topographical features. Urban planning, public health initiatives, and policymaking that try to lower air pollution all benefit from this knowledge. In this investigation, discrete runs were also conducted using GDAS 1 data (S\u0026aacute;nchez et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). With a top swing, this model permits sawing speed. of 0.1 cm/s. at 50 m AGL and bottom swing at 0 m AGL. It assumes a 1-hour unit size oscillation at 12 UTC of the day and an estimated 24-hour back diffusion with a mean period of 6 hours and an average layer above 100 m AGL. In this study, the online version HYSPLIT4.0 was used for trajectory and diffusion studies(Singh et al. 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Orbit: -\u003c/h2\u003e \u003cp\u003eNumerous orbital models have been created to study atmospheric motion. They can examine the oscillations of air. This work made use of the NOAA Hybrid Single Particle Lagrangian Integrated Trajectories (HYSPLIT4.0) model. Online at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arl.noaa.gov/HYSPLIT_info.php\u003c/span\u003e\u003cspan address=\"http://www.arl.noaa.gov/HYSPLIT_info.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e,\u003cem\u003e)\u003c/em\u003e the model is accessible (Goel and Guttikunda \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The model is used to calculate the initial position of the air returned from the receiving site at the start of the sampling period. Using the HYSPLIT4.0 model, a four-season continuous survey of Lucknow city was conducted to examine aerosol routes from different places up to 500 meters above ground level (Kov\u0026aacute;cs et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Long-range transmission of aerosols from various sources over 6 hours was studied. 48-hour mass isentropic back trajectories in Lucknow were computed in the investigation of long-term transport of aerosols from various places, at 6-hour intervals (Sadiq et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The RSPM allowable value recommended by the Central Pollution Control Board is 100 (\u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePollutant yearly Mean Concentration (in \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO at all seven monitoring Stations (i.e. Industrial, Residential, Commercial, area).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSTATION\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eCONCENTRATION (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003e(ANNUAL AVERAGE)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYEARS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHAZRATGANJ (COMMERCIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e211.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e216.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e226.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSARAI MALI KHAN (COMMERCIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e187.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e197.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eANSAL T.C. (COMMERCIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e201.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e166.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e177.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e187.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eGOMTI NAGAR (COMMERCIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e158.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e182.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e188.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMAHANAGAR (RESIDENTIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e204.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e189.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e189.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e199.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eALIGANJ (RESIDENTIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e148.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e168.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e178.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eTALKATORA (INDUSTRIAL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e224.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e221.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e269.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e299.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows annual averages concentration of air pollutants in different areas, such as commercial, residential, industrial over a study period. An air pollution's annual average concentration is the mean concentration of different dangerous compounds in the atmosphere over a specific period of time, usually a year. Particulate matter, carbon monoxide, nitrogen dioxide, and sulphur dioxide represent a few types of these contaminants. Monitoring air quality and any potential risks to people depends on the monitoring and reporting of these concentrations. In order to safeguard the environment and public health, regulatory authorities and environmental agencies frequently develop standards or guidelines for acceptable concentrations of particular contaminants. The actual average concentrations may vary significantly based on a number of variables, including geographic features, traffic density, industrial activity, location (rural vs. urban), and weather patterns. For the purpose of to track these concentrations and give information to the public, academics, and policymakers, monitoring stations and air quality indices are helpful (P. G. Kumar et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eIn the following sections, average concentration variations with PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO has been evaluated and discussed:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnnual average concentration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig.3(a) displays the yearly average PM\u003csub\u003e10\u003c/sub\u003e concentration over Lucknow City. At every monitoring site between 2016 and 2022, there is a significant amount of fluctuation. Hazratganj (323.05\u0026nbsp;\u0026micro;g-m\u003csup\u003e-3\u003c/sup\u003e) station had the highest yearly average concentration of any station in 2017 because of its high population density and substantial traffic problems. Hazratganj is a business neighborhood. Conversely, Ansal TC displayed varying attention during the study period, presumably as a result of the institution\u0026apos;s annual holidays on certain days.\u0026nbsp;Due to the Covid lockdown, several anthropogenic activities were shut down in 2020. As a result, Sarai Mali Khan and Gomti Nagar showed a gradual increase until 2017, with a peak concentration of 243.25 and 233.55 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in 2017, respectively, and a variation of 163.56 and 158.96 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e respectively till 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn 2020, the residential areas of Aliganj and Mahanagar exhibited a minimum concentration of 118.34 and 153.52\u0026micro;g-m\u003csup\u003e-3\u003c/sup\u003e, respectively. Due to the COVID-19 pandemic-related lockdown, there were also fewer anthropogenic activities and vehicle movements in the region, which contributed to the fall in concentrations. \u0026nbsp;Throughout the investigation, it was found that the Talkatora and Sarai Mali Khan stations had the highest yearly average concentration of SO\u003csub\u003e2\u003c/sub\u003e [Fig.3(b)]. It may be the result of the area\u0026apos;s heavy usage of fossil fuels, such as coal and oil. The industrial Talkatora station has the highest concentration in 2022 8.88 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, while the commercial Ansal TC station displays the lowest concentration 8.40 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in 2019 due to the closure of the institutional sector during the Covid shutdown. Because they have the highest population density relative to other commercial areas, residential neighbourhoods like Mahanagar and Aliganj also exhibit a notable concentration of SO\u003csub\u003e2\u003c/sub\u003e. [Fig.3(c)] illustrates the annual fluctuation of NO\u003csub\u003e2\u003c/sub\u003e, which abruptly decreased in 2019 as a result of the COVID-19 lockdown and continued to rise until 2022. The stations in Talkatora (an industrial region) and Hazratganj (a commercial district) have the highest concentrations due to the highest levels of traffic congestion, whereas Aliganj displays the lowest concentration surge (\u003cem\u003eCATALOGUE INDIAN EMISSION INVENTORY REPORTS\u003c/em\u003e 2022). The commercial areas of Ansal TC, Gomti Nagar, and Sarai Mali Khan exhibit an average increase in NO\u003csub\u003e2\u003c/sub\u003e concentration.\u0026nbsp;Annual variation of CO shows [Fig.3(d)] Talkatora (industrial), Hazratganj (commercial) and Ansal TC, (commercial) stations account for maximum concentrations of 33.67,30.69,31.87 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e respectively.\u0026nbsp;While Aliganj and Mahanagar, which are mainly residential areas, see only a small leap in concentration, those regions have the highest levels of traffic congestion\u0026nbsp;(Huangfu and Atkinson 2020). The commercial zones, Sarai Mali Khan, and Gomti Nagar show an average increase in CO concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Seasonal variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt each of the seven monitoring stations, the seasonal fluctuation in the pollutants\u0026apos; concentrations has been examined. Winter (December to February) and Summer are the seasons taken into consideration (March to June), post-monsoon (October to November), and Monsoon (June to September).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Variation in PM\u003csub\u003e10\u003c/sub\u003e\u003c/strong\u003e\u0026ndash;\u0026nbsp;Fig.4 shows the seasonal variation of PM\u003csub\u003e10\u003c/sub\u003e concentration (\u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e) and shows discernible changes during the research period. The post-monsoon season at Hazratganj station Fig.4(a) has the greatest PM\u003csub\u003e10\u003c/sub\u003e concentration, with winter, summer, and monsoon following suit. The CPCB threshold of 100 \u0026micro;g-m\u003csup\u003e-3\u003c/sup\u003e was exceeded by the maximum concentration of 447.46 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e for the post-monsoon of 2017 and the minimum concentration of 114.30 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e for the monsoon of 2016 (Chaurasia et al. 2018). Similar to this, the post-monsoon and winter values at Sarai Mali Khan, Gomti Nagar, and Talkatora [Fig.4(b), Fig.4(d), and Fig.4(g)] are nearly higher than those of the other two seasons. Elevated measurements during the post-monsoon and winter seasons may be attributed to the existence of locally generated and transported contaminants, in addition to the maintenance of stable conditions throughout the area during that period. Furthermore, a region-wide temperature inversion may occur, intensifying the pollutants and increasing their concentration over the research area. During the monsoon season, rainfall lowers the concentration, allowing pollutants to disperse throughout the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 SO\u003csub\u003e2\u003c/sub\u003e variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe concentration is below the 80\u0026micro;g m\u003csup\u003e-3\u0026nbsp;\u003c/sup\u003erecommended limits set by the Central Pollution Control Board, as indicated by the horizontal lines in Fig.5 and the seasonal variation of SO\u003csub\u003e2\u003c/sub\u003e depicted. The concentrations at Stations Hazratganj [Fig.5(a)] and Sarai Mali Khan [Fig.5(b)] during the post-monsoon season in 2017 peaked at 10.92 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e and 10.97 \u0026micro;g-m\u003csup\u003e-3\u003c/sup\u003e, respectively. On the other hand, Gomti Nagar [Fig.5(d)] displays the highest concentration during the winter of 2016 of 10.09 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, whereas Ansal TC [Fig.5(c)] has a highest concentration of 11.40 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e during the post-monsoon of 2019. In the winter of 2016, the maximum SO\u003csub\u003e2\u003c/sub\u003e concentrations at Mahanagar [Fig.5(e)] and Aliganj [Fig.5(f)] were 10.09 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e and 10.84 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, respectively, however they were lower. focus on other stations. In the post-monsoon season of 2017, Talkatora [Fig.5(g)] had a maximum of 11.06 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e. The usage of biomass as a residential fuel in cities may be the source of the elevated SO\u003csub\u003e2\u003c/sub\u003e concentrations observed in the winter and post-monsoon seasons. Significantly more SO\u003csub\u003e2\u003c/sub\u003e is produced at night as a result of the city burning biomass to fight the bitter cold. Similar to the commercial sector, the SO\u003csub\u003e2\u003c/sub\u003e content is higher there, most likely as a result of excessive biomass burning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 NO\u003csub\u003e2\u003c/sub\u003e variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The seasonal change in NO\u003csub\u003e2\u003c/sub\u003e concentration is seen in Fig.6. The NO\u003csub\u003e2\u003c/sub\u003e concentrations at Talkatora and Hazratganj and [Fig.6(g), Fig.6(a)] show a progressive increase during almost every season, peaking at 38.40 and 37.20 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in the post-monsoon of 2022. Sarai Mali Khan and Ansal TC [Fig.6(b), Fig.6(c)] show almost the same variation throughout the study; their highest concentrations were 35.00 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e during the winter of 2019 and 34.40 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e during the post-monsoon of 2020, respectively. The summer of 2019 is when Gomti Nagar [Fig.6(d)] reaches its highest concentration of 33.33 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e. Peak concentrations were recorded in Mahanagar and Aliganj [Fig.6(e), Fig.6(f)] during the post-monsoon of 2020 and the winter of 2019, respectively, at 34.90 and 35.40 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e. In comparison to other commercial and residential monitoring locations, there might be less vehicle traffic on this industrial site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.4 CO variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;shows how the concentration of CO varies with the season. The CO concentrations in Hazratganj and Talkatora [Fig. 7(a), Fig.7(g)] show a progressive increase across almost every season, peaking at 37.71 and 37.0 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e during the post-monsoon in 2022. \u0026nbsp;Ansal TC and Sarai Mali Khan [Fig.7(c), Fig.7(b)] show almost the same variation throughout the study; their peak concentrations were 37.67 during the winter of 2022 and 36.30 \u0026micro;g-m\u003csup\u003e-3\u003c/sup\u003e during the post-monsoon of 2022, respectively. Gomti Nagar [Fig.7(d)] gets its peak concentration of 34.45 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in the summer of 2017. Peak concentrations were measured at 36.03 and 36.00 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in Mahanagar and Aliganj [Fig.7(e), Fig.7(f)] in the winter of 2022 and the post-monsoon of 2022, respectively. In comparison to other commercial and residential monitoring locations, there might be higher vehicle traffic on this industrial site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Area-wise variation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig.8\u0026nbsp;shows the area-wise fluctuation of PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO concentrations for all monitoring stations. \u0026nbsp;When it comes to PM\u003csub\u003e10\u0026nbsp;\u003c/sub\u003econcentration at commercial stations [Fig. 8(a)], Hazratganj is in the lead among other stations, having peaked in 2017 at 323.05 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e. Other stations that follow include Gomti Nagar, Ansal TC, and Sarai Mali Khan. After 2017, Talkatora, an industrial station, had the highest concentration of PM\u003csub\u003e10\u003c/sub\u003e of any place. This could be attributed to an increase in industrial activity in Lucknow city\u0026apos;s surrounding districts. In 2018, Talkatora recorded a maximum concentration of 229.43 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, whereas Aliganj and Mahanagar had minimal concentrations of 174.72 and 174.75 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, respectively, in 2019 and 2018.\u0026nbsp;Sarai Mali Khan has the highest SO\u003csub\u003e2\u003c/sub\u003e concentration [Fig.8(b)], at 8.94 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, followed by Hazratganj, Gomti Nagar, and Ansal TC, which is for commercial zones. Nonetheless, compared to the other locations, the industrial zones had a higher concentration. The industrial region of Talkatora has the highest concentration 9.13 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e, followed by the residential areas of Mahanagar and Aliganj. Hazratganj and Sarai Mali Khan exhibit competition for variance in NO\u003csub\u003e2\u003c/sub\u003e concentration [Fig.8(c)], with the former having the highest concentration of 33.01 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in 2022 and the latter having the highest concentration of 31.86 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in 2019. Hazratganj and Talkatora indicate competition for variation in CO concentration [Fig.8(d)], with the former having a highest concentration of 31.00 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in 2021 and the latter having a maximum concentration of 32 \u0026micro;g m\u003csup\u003e-3\u003c/sup\u003e in 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Dispersion Model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dispersion of pollutants in Lucknow city has been studied using the HYSPLIT4.0 dispersion modelling. Pollution dispersion is used in HYSPLIT4.0 dispersion modelling for the winter, summer, monsoon, and post-monsoon seasons. The online version of HYSPLIT4.0 employs Backward dispersion for 24 hours, with average intervals of 6 hours each, to disperse pollutants. figures Fig.9(a) for winter, Fig.9(b) for summer, Fig.9(c) for monsoon, and Fig.9(d) for post-monsoon display all of the results. The star emblem, which has four concentric rings spaced 50 kilometres apart, represents the city of Lucknow. Fig 9(a) for winter, Fig.9(b) for summer, Fig. 9(c) for monsoon, and Fig. 9(d) for post-monsoon display all of the results. The star emblem, which has four concentric rings spaced 50 kilometres apart, represents the city of Lucknow. The findings show Fig.10 that during the winter and post-monsoon seasons, the plumes are advocating from the northwest, while during the monsoon and summer seasons, they are advocating from the east-south.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe analysis of changes in the RSPM, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO yearly average concentrations in Lucknow city between 2016 and 2022 was presented in this work. For this study, Hazratganj, Aliganj, Talkatora, Sarai Mali Khan, Gomti Nagar, Mahanagar, and Ansal TC were taken into consideration. It has been investigated how the concentrations of all four pollutants\u0026mdash;RSPM, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO vary annually, seasonally, and area-wise. At each of the seven monitoring stations, the annual average concentration of RSPM ranges from 148.74 to 323.05 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, SO\u003csub\u003e2\u003c/sub\u003e from 7.11 to 8.94 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e from 23.52 to 31.86 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, and CO from 27.38 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003eto 33.89 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. It was found that the RSPM and CO concentrations were above the CPCB-recommended limit. Conversely, it was found that the SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e concentrations were below the recommended levels. Based on a comparison of seasonal variations, the highest recorded RSPM concentration during the post-monsoon season falls between 160.15 and 447.47 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. Due to the rain at every monitoring site, the concentration is lower during the monsoon season than it is during the other seasons. For every season, the range of SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO concentrations was recorded to be 5.55\u0026ndash;10.94 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, 20.23\u0026ndash;38.40 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, and 27.38\u0026ndash;33.89 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eAccording to area-wise concentration, industrial locations have higher levels of pollution than commercial and residential areas. According to the statistics gathered, Lucknow city's pollution level for 2022 will significantly fall as a result of the lockdown brought on by the COVID-19 epidemic. Even during the COVID-19 Lockdown, the study period's annual average concentration of RSPM was found to be high at all stations and over the recommended level of 100 \u0026micro;g-m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. Based on trajectory analysis, winds that blow from a north-westerly direction all year round are mostly responsible for the migration of aerosols from developed areas and cities. The present study shows that Lucknow's air quality is negatively impacted by pollution, which is not only generated locally but also mostly transported from nearby industrial and urbanized regions. This might be the reason for the declining air quality in the city despite measures taken by the Indian government or the local municipal authority. The dispersion analysis carried out with HYSPLIT4.0 for examples chosen based on the highest concentration for various seasons in the city across the research period has likewise supported this result. The RSPM time-series data indicates the presence of both yearly and semi-annual periodicity throughout the research period. However, in addition to theoretical and experimental understanding of source allocation studies and pollutant discharge across the region, more data collection and analysis in the Lucknow metropolitan area is required. When compared to SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e pollutants, the results show that lowering particulate matter and carbon monoxide pollution in the city is a serious problem and is at a critical stage.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRSPM - Respirable Suspended Particulate Matter\u003c/p\u003e\n\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e - Particulate matter particle size less than 10 mm\u003c/p\u003e\n\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e - Sulphur dioxide\u003c/p\u003e\n\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e - Nitrogen dioxide\u003c/p\u003e\n\u003cp\u003eCPCB - Central Pollution Control board\u003c/p\u003e\n\u003cp\u003eNCEP - National Centres for Environment Prediction\u003c/p\u003e\n\u003cp\u003eGDAS - Global Data Assimilation System\u003c/p\u003e\n\u003cp\u003eAGL - Above Ground Level\u003c/p\u003e\n\u003cp\u003eNAMP - National Ambient Monitoring Programme\u003c/p\u003e\n\u003cp\u003eHYSPLIT4.0 - Hybrid Single-Particle Lagrangian Integrated Trajectory model version 4\u003c/p\u003e\n\u003cp\u003eNOAA - National Oceanic and Atmospheric Administration\u003c/p\u003e\n\u003cp\u003eNCAR - National Centre for Atmospheric Research\u003c/p\u003e\n\u003cp\u003eRDS - Respirable dust sampler\u003c/p\u003e\n\u003cp\u003eSAMP – State Ambient Monitoring Programme\u003c/p\u003e\n\u003cp\u003eUPPCB – Uttar Pradesh Pollution Control Board\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to extend their gratefulness to the Civil Engineering Department, Institute of Engineering and Technology Lucknow for providing the guidance and support that are required to conduct the study, Uttar Pradesh Pollution Control Board (UPPCB), Central Pollution Control Board (CPCB), and New Delhi for supporting the projects under Programs for State and National Air Quality Monitoring (SAMP) and NAMP (National Air Quality Monitoring).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVipin Kumar:\u003c/strong\u003e Data curation, Investigation, Methodology, Writing – original draft, Writing – review \u0026amp; editing, Validation, Software investigation. \u003cstrong\u003ePrabhat Kumar Patel:\u0026nbsp;\u003c/strong\u003eSupervision, Validation, Visualization, Writing – review \u0026amp; editing, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting Interests:\u003c/strong\u003e All contributors declare that no relationships of interest occur.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of Funding Sources:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or non-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement: \u0026nbsp;\u003c/strong\u003eThe author disclosed that there is no human or animal heart in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAn, F., Liu, J., Lu, W., \u0026amp; Jareemit, D. 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A coupled macroscopic traffic and pollutant emission modelling system for Barcelona. \u003cem\u003eTransportation Research Part D: Transport and Environment\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e. https://doi.org/10.1016/j.trd.2021.102725\u003c/li\u003e\n \u003cli\u003eSadiq, A., El Fazziki, A., Ouarzazi, J., \u0026amp; Sadgal, M. (2016). Towards an agent based traffic regulation and recommendation system for the on-road air quality control. \u003cem\u003eSpringerPlus\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(1). https://doi.org/10.1186/s40064-016-3282-2\u003c/li\u003e\n \u003cli\u003eSaini, D., Mishra, N., \u0026amp; Lataye, D. H. (n.d.). Variation of ambient air pollutants concentration over Lucknow city, trajectories and dispersion analysis using HYSPLIT4.0. https://doi.org/10.1007/s12046-022-02001-2S\u003c/li\u003e\n \u003cli\u003eS\u0026aacute;nchez, J. M., Ortega, E., L\u0026oacute;pez-Lambas, M. E., \u0026amp; Mart\u0026iacute;n, B. (2021a). 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GIS-Based On-Road Vehicular Emission Inventory for Lucknow, India. \u003cem\u003eJournal of Hazardous, Toxic, and Radioactive Waste\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4). https://doi.org/10.1061/(asce)hz.2153-5515.0000244\u003c/li\u003e\n \u003cli\u003eSingh, D., Shukla, S. P., Sharma, M., Behera, S. N., Mohan, D., Singh, N. B., \u0026amp; Pandey, G. (2016b). GIS-Based On-Road Vehicular Emission Inventory for Lucknow, India. \u003cem\u003eJournal of Hazardous, Toxic, and Radioactive Waste\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4). https://doi.org/10.1061/(asce)hz.2153-5515.0000244\u003c/li\u003e\n \u003cli\u003eWang, Y., Liang, X., Wang, Y., \u0026amp; Yu, H. (2017). Effects of Viscosity Index Improver on Morphology and Graphitization Degree of Diesel Particulate Matter. In \u003cem\u003eEnergy Procedia\u003c/em\u003e (Vol. 105, pp. 4236\u0026ndash;4241). Elsevier Ltd. https://doi.org/10.1016/j.egypro.2017.03.910\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HYSPLIT4.0, Vehicular emission, PM2.5, SO2, NO2, CO, Lucknow","lastPublishedDoi":"10.21203/rs.3.rs-4295589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4295589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the mean daily concentrations of oxides of carbon (CO), sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), and respirable suspended particles (RSPM-PM\u003csub\u003e10\u003c/sub\u003e) at seven monitoring stations in Lucknow city: Hazratganj, Gomti Nagar, Talkatora, Aliganj, Sarai Mali Khan, Mahanagar, and Ansal TC. The data is collected between 2016 and 2022. Based on the analysis, it can be observed that at all locations, the yearly average concentration of RSPM varies between 148.74 and 323.05 µg m\u003csup\u003e-3\u003c/sup\u003e, SO\u003csub\u003e2\u003c/sub\u003e between 7.11 and 8.94 µg m\u003csup\u003e-3\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e between 23.52 and 31.86 µg m\u003csup\u003e-3\u003c/sup\u003e, and CO between 27.38 and 33.89 µg m\u003csup\u003e-3\u003c/sup\u003e. The study of seasonal variation reveals that the lowest recorded concentration of RSPM was 81.59 µg m\u003csup\u003e-3\u003c/sup\u003e during the monsoon, while the highest recorded concentration was 447.47 µg m\u003csup\u003e-3\u003c/sup\u003e during the post-monsoon. On the other hand, seasonal variations in SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e were found to be below the recommended requirements, ranging from 5.55 to 10.94 µg m\u003csup\u003e-3\u003c/sup\u003e and 20.23 to 38.40 µg m\u003csup\u003e-3\u003c/sup\u003e, respectively. The COVID-19 lockout in 2020 caused the pollution level to somewhat decline, but it did not fall below the recommended limit for CO and RSPM. The Indian government has banned factories and implemented various measures within the city, but the levels of CO and PM\u003csub\u003e10\u003c/sub\u003e in Lucknow are not decreasing. The Trajectory and Dispersion study of the HYSPLIT4.0 model indicates that the wind, which blows from the northwest, carries pollutants from close by areas to maintain daily pollution levels above the Central Pollution Control Board's guidelines (i.e., 100 µg m\u003csup\u003e-3\u003c/sup\u003e). This suggests that there is insufficient local pollution control. The results suggest that controlling particulate matter and carbon monoxide pollution in the city is a serious challenge and has an alarming situation as compared to SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e pollutants.\u003c/p\u003e","manuscriptTitle":"Change in the concentration of pollutants in the air over the city of Lucknow, together with HYSPLIT4.0's trajectory and dispersion analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 08:54:43","doi":"10.21203/rs.3.rs-4295589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4636a468-c3d0-4638-b52d-09fd4013665d","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-16T08:54:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 08:54:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4295589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4295589","identity":"rs-4295589","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}