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Rautela KS, Goyal MK. Transforming air pollution management in India with AI and machine learning technologies. Sci Rep 2024; 14:20412. [PMID: 39223178 PMCID: PMC11369276 DOI: 10.1038/s41598-024-71269-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024] Open
Abstract
A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural-urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28-30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.
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Affiliation(s)
- Kuldeep Singh Rautela
- Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India
| | - Manish Kumar Goyal
- Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India.
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Khatri P, Hayasaka T, Holben BN, Singh RP, Letu H, Tripathi SN. Increased aerosols can reverse Twomey effect in water clouds through radiative pathway. Sci Rep 2022; 12:20666. [PMID: 36450848 PMCID: PMC9712532 DOI: 10.1038/s41598-022-25241-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/28/2022] [Indexed: 12/02/2022] Open
Abstract
Aerosols play important roles in modulations of cloud properties and hydrological cycle by decreasing the size of cloud droplets with the increase of aerosols under the condition of fixed liquid water path, which is known as the first aerosol indirect effect or Twomey-effect or microphysical effect. Using high-quality aerosol data from surface observations and statistically decoupling the influence of meteorological factors, we show that highly loaded aerosols can counter this microphysical effect through the radiative effect to result both the decrease and increase of cloud droplet size depending on liquid water path in water clouds. The radiative effect due to increased aerosols reduces the moisture content, but increases the atmospheric stability at higher altitudes, generating conditions favorable for cloud top entrainment and cloud droplet coalescence. Such radiatively driven cloud droplet coalescence process is relatively stronger in thicker clouds to counter relatively weaker microphysical effect, resulting the increase of cloud droplet size with the increase of aerosol loading; and vice-versa in thinner clouds. Overall, the study suggests the prevalence of both negative and positive relationships between cloud droplet size and aerosol loading in highly polluted regions.
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Affiliation(s)
- Pradeep Khatri
- grid.69566.3a0000 0001 2248 6943Center for Atmospheric and Oceanic Studies, Tohoku University, Sendai, Japan
| | - Tadahiro Hayasaka
- grid.69566.3a0000 0001 2248 6943Center for Atmospheric and Oceanic Studies, Tohoku University, Sendai, Japan
| | - Brent N. Holben
- grid.133275.10000 0004 0637 6666National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, USA
| | - Ramesh P. Singh
- grid.254024.50000 0000 9006 1798School of Life and Environmental Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA USA
| | - Husi Letu
- grid.9227.e0000000119573309Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
| | - Sachchida N. Tripathi
- grid.417965.80000 0000 8702 0100Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, India
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Omokungbe OR, Fawole OG, Owoade OK, Popoola OAM, Jones RL, Olise FS, Ayoola MA, Abiodun PO, Toyeje AB, Olufemi AP, Sunmonu LA, Abiye OE. Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors. Heliyon 2020; 6:e04207. [PMID: 32577574 PMCID: PMC7305390 DOI: 10.1016/j.heliyon.2020.e04207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 02/13/2020] [Accepted: 06/10/2020] [Indexed: 11/29/2022] Open
Abstract
The concentrations of fine and coarse fractions of airborne particulate matter (PM) and meteorological variables (wind speed, wind direction, temperature and relative humidity) were measured at six selected locations in Ile Ife, a prominent university town in Nigeria using a network of low-cost air quality (AQ) sensor units. The objective of the deployment was to collate baseline air quality data and assess the impact of prevailing meteorological conditions on PM concentrations in selected residential communities downwind of an iron smelting facility. The raw data obtained from OPC-N2 of the AQ sensor units was corrected using the RH correction factor developed based k-Kohler theory. This PM (corrected) fast time resolution data (20 s) from the AQ sensor units were used to create daily averages. The overall mean mass concentrations for PM2.5 and PM10 were 213.3, 44.1, 23.8, 27.7, 20.2 and 41.5 μg/m3 and; 439.9, 107.1, 55.0, 72.4, 45.5 and 112.0 μg/m3 for Fasina (Iron-Steel Smelting Factory, ISSF), Modomo, Eleweran, Fire Service, O.A.U. staff quarters and Obafemi Awolowo University Teaching and Research Farm (OAUTRF), respectively. PM concentration and wind speed showed a negative exponential distribution curve with the lowest exponential fit coefficient of determination (R2) values of 0.08 for PM2.5 and 0.03 for PM10 during nighttime periods at Eleweran and Fire service sites, respectively. The relationship between PM concentration and temperature gave a decay curve indicating that higher PM concentrations were observed at lower temperatures. The exponential distribution curve for the relationship between PM concentration and relative humidity (RH) showed that PM concentrations do not vary for RH < 80 % while stronger relationship was noticed with higher PM concentration for RH > 80 % for both day and nighttime. The performances of the MLR model were slightly poor and as such not too reliable for predicting the concentration but useful for improving predictive model accuracy when other variables contributing to the variability of PM is considered. The study concluded that the anthropogenic and industrial activities at the smelting factory contribute significantly to the elevated PM mass concentration measured at the study locations.
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Affiliation(s)
- Opeyemi R Omokungbe
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | - Olusegun G Fawole
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria.,Atmospheric Science Unit, Department of Environmental Sciences, Stockholm University, SE-11418 Stockholm, Sweden
| | - Oyediran K Owoade
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | | | - Roderic L Jones
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Felix S Olise
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | - Muritala A Ayoola
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | - Pelumi O Abiodun
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | - Adekunle B Toyeje
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | - Ayodele P Olufemi
- Department of Physics, University of Medical Sciences, Ondo, Nigeria
| | - Lukman A Sunmonu
- Environmental Pollution Research Laboratory, Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife 230001, Nigeria
| | - Olawale E Abiye
- Centre for Energy Research and Development (CERD), Obafemi Awolowo University, Ile-Ife, Nigeria
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On a Flood-Producing Coastal Mesoscale Convective Storm Associated with the Kor’easterlies: Multi-Data Analyses Using Remotely-Sensed and In-Situ Observations and Storm-Scale Model Simulations. REMOTE SENSING 2020. [DOI: 10.3390/rs12091532] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
A flood-producing heavy rainfall event occurred at the mountainous coastal region in the northeast of South Korea on 5–6 August 2018, subsequent to extreme heat waves, through a quasi-stationary mesoscale convective system (MCS). We analyzed the storm environment via a multi-data approach using high-resolution (1-km) simulations from the Weather Research and Forecasting (WRF) and in situ/satellite/radar observations. The brightness temperature, from the Advanced Himawari Imager water vapor band, and the composite radar reflectivity were used to identify characteristics of the MCS and associated precipitations. The following factors affected this back-building MCS: low-level convergence by the Korea easterlies (Kor’easterlies), carrying moist air into the coast; strong vertical wind shear, making the updraft tilted and sustained; coastal fronts and back-building convection bands, formed through interactions among the Kor’easterlies, cold pool outflows, and orography; mid-level advection of cold air and positive relative vorticity, enhancing vertical convection and potential instability; and vigorous updraft releasing potential instability. The pre-storm synoptic environment provided favorable conditions for storm development such as high moisture and temperature over the coastal area and adjacent sea, and enhancement of the Kor’easterlies by expansion of a surface high pressure system. Upper-level north-northwesterly winds prompted the MCS to propagate south-southeastward along the coastline.
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Lagrangian Cloud Tracking and the Precipitation-Column Humidity Relationship. ATMOSPHERE 2018. [DOI: 10.3390/atmos9080289] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The tropical, oceanic mean relationship between column relative humidity and precipitation is highly non-linear. Mean precipitation remains weak until it rapidly picks up and grows at high column humidity. To investigate the origin of this relationship, a Lagrangian cloud tracking code, RAMStracks, is developed, which can follow the evolution of clouds. RAMStracks can record the morphological properties of convective clouds, the meteorological environment of clouds, and their effects. RAMStracks is applied to a large-domain radiative convective equilibrium simulation, which produces a complex population of convective clouds. RAMStracks records the lifecycle of 501 clouds through growth, splits, mergers, and decay. The mean evolution of all these clouds is examined. It is shown that the column humidity evolves non-monotonically, but that lower-level and upper-level contributions to total moisture do evolve monotonically. The precipitation efficiency of tropical storms tends to increase with cloud age. This is confirmed using a prototype testing method. The same method reveals that different tracked clouds with similar initial conditions evolve in very different ways. This makes drawing general conclusions from individual storms difficult. Finally, the causality of the precipitation-column humidity relationship is examined. A Granger Causality test, as well as regressions, suggest that moisture and precipitation are causally linked, but that the direction of causality is ambiguous. Much of this link appears to come from the lower-level moisture’s contribution to column humidity.
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Ladino LA, Korolev A, Heckman I, Wolde M, Fridlind AM, Ackerman AS. On the role of ice-nucleating aerosol in the formation of ice particles in tropical mesoscale convective systems. GEOPHYSICAL RESEARCH LETTERS 2017; 44:1574-1582. [PMID: 29551842 PMCID: PMC5852679 DOI: 10.1002/2016gl072455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Over decades, the cloud physics community has debated the nature and role of aerosol particles in ice initiation. The present study shows that the measured concentration of ice crystals in tropical mesoscale convective systems exceeds the concentration of ice nucleating particles (INPs) by several orders of magnitude. The concentration of INPs was assessed from the measured aerosol particles concentration in the size range of 0.5 to 1 µm. The observations from this study suggest that primary ice crystals formed on INPs make only a minor contribution to the total concentration of ice crystals in tropical mesoscale convective systems. This is found by comparing the predicted INP number concentrations with in-situ ice particle number concentrations. The obtained measurements suggest that ice multiplication is the likely explanation for the observed high concentrations of ice crystals in this type of convective system.
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Affiliation(s)
- Luis A. Ladino
- Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
| | - Alexei Korolev
- Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
| | - Ivan Heckman
- Cloud Physics and Severe Weather Research Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
| | - Mengistu Wolde
- Flight Research Laboratory, National Research Council of Canada, Ottawa, Ontario, Canada
| | - Ann M. Fridlind
- NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10027, USA
| | - Andrew S. Ackerman
- NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10027, USA
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