Air Quality Monitoring Program Research Articles

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Assessing Air Quality Using Multivariate Statistical Approaches

The purpose of the current study was to evaluate air quality in Dong Thap province, Vietnam. The air quality data was collected during 2019–2020, representing the time of pre- and mid-COVID-19. Twenty-seven air quality samples (in the areas of urban, residential-administrative, hospital-schools, and industry-craft village areas) were used for the evaluation. Air quality was evaluated using national technical regulations on air quality, including QCVN 26:2010/BTNMT and QCVN 05:2013/BTNMT. The difference of mean air quality between the areas was examined using a one-way ANOVA followed by the Duncan test at a significant level of 5%. The relationship between air quality parameters and microclimate factors was tested using Pearson correlation. Principal component analysis (PCA) was utilized to identify critical variables and potential sources of air variation. Cluster analysis (CA) was applied to group similar air quality sites, thus recommending air monitoring site selection. The results show that the air quality in the study area is not polluted. The concentrations of noise, TSP, SO2, and NO2in the mid-COVID-19 pandemic were significantly lower than those in the pre-COVID-19 pandemic due to the social distancing policy. There was a close correlation among air quality parameters, except for air humidity. PCA identified two to four potential sources of air variation, explaining 84.3%, 100%, 100% and 89.7% of the total air quality variance at urban, residential–administrative, hospital-schools, and industry-craft villages, respectively. CA divided the 27 sampling sites into eight groups by the differences, mainly in humidity, wind speed noise, TSP, and CO. Eight sampling sites could be potentially reduced from the current monitoring program for representativeness and cost-effectiveness purposes. All air parameters in the current study are significant for monitoring, and the potential sources of air quality variation are traffic activities, industrial production, craft village activities, and daily life using fuels in residential areas. The results of the current study provide useful information for air quality monitoring and management. Future monitoring programs should include toxic air pollutants in air quality monitoring programs. Doi: 10.28991/CEJ-2024-010-02-012 Full Text: PDF

Dataset from long-term air quality monitoring in the World Natural Heritage Geirangerfjord, Western Norway (AQM-G)

Here, we present a datapaper containing observational air quality and meteorological data related to our long-term air quality monitoring program at the UNESCO Natural World Heritage Site Geirangerfjord, Western Norway. The dataset will be updated with future data.

The evolution of atmospheric particulate matter in an urban landscape since the Industrial Revolution

Atmospheric particulate matter (PM) causes 3.7 million annual deaths worldwide and potentially damages every organ in the body. The cancer-causing potential of fine particulates (PM2.5) highlights the inextricable link between air quality and human health. With over half of the world’s population living in cities, PM2.5 emissions are a major concern, however, our understanding of exposure to urban PM is restricted to relatively recent (post-1990) air quality monitoring programmes. To investigate how the composition and toxicity of PM has varied within an urban region, over timescales encompassing changing patterns of industrialisation and urbanisation, we reconstructed air pollution records spanning 200 years from the sediments of urban ponds in Merseyside (NW England), a heartland of urbanisation since the Industrial Revolution. These archives of urban environmental change across the region demonstrate a key shift in PM emissions from coarse carbonaceous ‘soot’ that peaked during the mid-twentieth century, to finer combustion-derived PM2.5 post-1980, mirroring changes in urban infrastructure. The evolution of urban pollution to a recent enhanced PM2.5 signal has important implications for understanding lifetime pollution exposures for urban populations over generational timescales.

Covariance in policy diffusion: Evidence from the adoption of hyperlocal air quality monitoring programs by US cities

The diffusion of urban policy may occur through multiple mechanisms with complex interactions that obscure the interpretation of independent effects. In this work, we present a case study to explore the potential impact of covariance on the identification of the independent effects of the explanatory variables. We explore the influence of multiple diffusion mechanisms: learning, imitation, and coercion; policy mobility factors; and internal determinants on the likelihood of hyperlocal air quality monitoring program (HAMP) adoption by large cities in the US with a population >300,000 over the past decade. In general, results imply the adoption of HAMPs over the past decade has been motivated by sociopolitical rather than environmental goals. However, comparing the outcomes of a common, but limited, method to detect covariance and those from an expanded systematic variable selection exploration, shows that a third of the variables have covariance effects that can alter their impact and level of significance. The absence of coercion is found to lead to half of the variables exhibiting different results. These results highlight the importance of variable selection methods in determining a variable's significance and impact on the likelihood of policy adoption.

Update of Ambient Air Pollution Monitoring Programs within Regional-Level Implementation of National Projects

Introduction: The Federal Clean Air Project has been developed and is now implemented with the purpose of improving both ambient air quality and the quality of life of the population in 12 pilot cities, including Bratsk. An adequate air pollution control program, priority pollutant monitoring, and health risk management can facilitate improvement of the quality of human life and achievement of the targets of government projects. Objective: To update and streamline the ambient air quality monitoring program in Bratsk within implementation of national and federal projects at the regional level. Materials and methods: The updated air quality control program in Bratsk was based on the results of dispersion modeling verified by instrumental measurements of 34 pollutants at five monitoring stations of the Federal Service for Hydrometeorology and Environmental Monitoring (Roshydromet) and three stations of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor). The consolidated database included 2,202 sources emitting 112 chemicals, with total emissions approaching 127,207 tons per year. Hygienic analysis and health risk assessment were performed according to conventional procedures. Results: We established that airborne levels of 29 pollutants exceeded their maximum allowable concentrations (MAC), i.e. were up to 7.0 times higher than short-term MAC, 13.8 and 60 times higher than the average daily and annual MAC, respectively. We also found that 19 air contaminants posed unacceptable health risks for the local population (CR up to 1.82×10–3, HQac up to 113.2, HQcr up to 211.1). Cluster analysis of the results of health risk assessment allowed us to distinguish three clusters within the urban area. We substantiated cutting down the number of monitoring stations to one and its optimal location in Cluster 1, the expediency of moving the station in Cluster 2 and adding a station in Cluster 3 at the point of concern. Optimization of the air quality control program involves reducing the number of monitored pollutants to 8–15 chemicals that are not covered by Roshydromet air monitoring plan. Conclusions: Given the changing sanitary and epidemiological situation, streamlining of air quality monitoring programs will provide relevant and timely information, thus contributing to elaboration of effective health risk management activities.

Assessment of Air Quality Around Heavy Traffic Zone in Hyderabad

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city. Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB .The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 5 stations based heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 3 PM2.5,range 183.7 – 163.26µg/m3 is very poor as per AQI, PM10 range 151.11 to 137.76 µg/m3 is moderately polluted as per AQI, NO2 range 61.03 to 76.4 µg/m3 is satisfied as per AQI, NO3 range 1.196 to 7.96 is good, SO2 is 6.34 to 55.83 µg/m3 is satisfied ,CO 70.35 to 71.97 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Air Quality Monitoring at Heavy Traffic Zone in Hyderabad

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city.Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB.The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 2 stations Newmalakpet and Sanathnagar based on heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 52PM2.5,range 120 to 249 µg/m3 is very poor as per AQI, PM10 range 101.2 to 171 µg/m3 is moderately polluted as per AQI, NO2 range 7.46 to 32.9 µg/m3 is good as per AQI, NH3 range3.06 to 5.44 is good as per AQI, SO2 is 12.97 to 8.53 µg/m3 is good ,CO 61.83 to 71.9 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Analysis of Water and Air Quality in and Around HIMSWM Treatment Plant- Hyderabad

In recent days the emission of air pollutants in to the environment has grown very quickly. The air pollution is caused due to the smoke emitted by vehicles, industries and other sources. Hyderabad is the largest and capital city of Telangana. It occupies 650km2 on the Deccan Plateau along the banks of the Musiriver, in the northern part of south India. According to the 2011 Census of India, Hyderabad is the fourth-most populous city in India with a population of 6.9 million residents within the city limits, and has a population of 9.7 million residents in the metropolitan region, making it the sixth-most populous metropolitan area in India. With an output of US$74 billion, Hyderabad has the fifth-largest urban economy in India. In India air pollution is monitored by Central Pollution Control Board(CPCB) along with State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with seven sensor stations 248 towns and cities have the air quality network of 591 air quality monitoring stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing the number to 883 to achieve the goal of 1500 by 2024. For our study because of some practical difficulties and analysis of the air quality. Air Pollutants has been collected from the CPCB. The dataset contains City, Date, Time, PM2.5,PM10, NO2, SO2, CO, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into six zones, air quality monitoring sensors are located at 14 places in and around six zones. For our study we selected one sensor that is Secunderabad, which covers the surrounding areas including Jawahar Nagar treatment plant. To analysis the maximum concentration of air pollutants such as PM2.5,PM10,SO2,NO2,CO,O3over the time period of three months (Jan to Mar 2023).Most physio-chemical properties, including as pH, EC, Alkalinity, Chlorides, Cu, Mn, Pb, and Cr, exceeded their acceptable limits at more than three sample sites, according to this study. The water sources were determined to be unsafe for drinking, agricultural, and irrigation uses due to elevated toxicity levels.It was observed that PM2.5,ranges 218 - 403µg/m3 is very poor causes respiratory illness, PM10 range 112 - 360µg/m3 is moderately polluted causes breathing discomfort ( Asthma, lungs), NO2 range 10 – 12ppm is satisfied causes minor breathing discomfort to sensitive people ,O3 range 20 – 35ppm is good causes minor effect, SO2 is 12 – 22ppm is satisfied causes minor breathing discomfort to sensitive people ,CO 109 – 119ppm is severe effects on healthy people and serious impact for those with existing diseases, so continuous air quality monitoring is necessary to protect environment and human health

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring.

Model-based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such that our approach can be adopted to design realistically sized geostatistical studies. This is demonstrated through a simulated study and through designing an air quality monitoring program in Queensland, Australia. The results show that our designs remain highly efficient in achieving each experimental objective individually, providing an ideal compromise between the two objectives. Accordingly, we advocate that our approach could be adopted more generally in model-based geostatisticaldesign.

Spatial epidemiology of acute respiratory infections in children under 5 years and associated risk factors in India: District-level analysis of health, household, and environmental datasets.

In India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India. Data on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors. Acute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI. To decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

The Air Quality Monitoring Tool Based on Internet of Things to Monitor Pollution Emissions Continuously

As a result of pollutant emissions, air pollution occurs which causes various problems such as global climate change, environmental degradation, and diseases due to reduced air quality such as respiratory infections, asthma, and even lung cancer. Polluted air cannot be insulated; therefore, a tool is needed to monitor air quality. So far, in Indonesia the tool for monitoring air quality in the form of an ISPU (Indeks Standar Pencemaran Udara = Air Pollution Standard Index) monitoring station submitted the data daily or not continuously. Whereas air pollution creating from emissions needs to be carried out continuously, so that in 2021, the Minister of Environment and Forestry issued a regulation on the Information System for Continuous Emission Monitoring System (CEMS). Monitoring using CEMS is deemed necessary because correct, accurate, and continuous emission data and information need to be carried out to find out emissions in an integrated manner. This research was conducted to develop CEMS based on internet of things. Pollutant sensors in the form of methane (CH₄), butane (C₄H₁₀), carbon monoxide (CO), particulate (PM_2.5), and ammonia (NH₃) are assembled on the Arduino Uno microcontroller. MQ-4 sensors are used for measuring methane (CH₄), MQ-2 sensors for butane (C₄H₁₀), MQ-7 sensors for carbon monoxide (CO), GP2Y1010AU0F sensors for particles (PM_2.5), and MQ-137 sensors for ammonia (NH₃). Data from the sensor is transmitted to the Liquid Crystal Display (LCD) via the Arduino Uno microcontroller, where it is shown on smartphones as numbers. This web-based air quality monitoring program should continuously track air pollution emissions. The main finding shows that the concentration of particulate matter has the highest concentration (above the emission quality standard) among other sensors, namely 52.29 µg/m³ on June 15, 2022 in South Tangerang, Indonesia. Based on these results, air quality monitoring for (PM_2.5) should be checked periodically.

Effectiveness of the National Pollutant Release Inventory as a Policy Tool to Curb Atmospheric Industrial Emissions in Canada

To curb greenhouse gas emissions and reduce atmospheric pollutants in Canada, many pieces of environment legislation are targeted at reducing industrial emissions. Traditional regulation prescribes penalties through fines to discourage industries from polluting, but, in the past two decades, alternative forms of environmental regulation, such as the National Pollutant Release Inventory (NPRI), have been introduced. NPRI is an information management tool which requires industries to self-report emissions data based on a set of guidelines determined by Environment and Climate Change Canada, a federal agency. The tool works to inform the public regarding industry emissions and provides a database that can be analyzed by researchers and regulators to inform emissions trends in Canada. These tools have been successful in other jurisdictions (e.g., United States and Australia). However, research assessing the U.S. Toxic Release Inventory suggests there are fundamental weaknesses in the self-reported nature of the data and incidences of under-reporting. This preliminary study aimed to explore NPRI in Canada and test its effectiveness against the National Air Pollutant Surveillance Network (NAPS), an air quality monitoring program administered by the federal government. While instances of under-reporting were undetected, this study identified areas of weakness in the NPRI tool and instances of increasing emissions across various industrial sectors in Canada.

Does improvement of environmental information transparency boost firms’ green innovation? Evidence from the air quality monitoring and disclosure program in China

Transparent environmental information is fundamental for the government to better regulate pollution and for the public to be aware of pollution. However, how the improvement of environmental information transparency affects firms' environmental behavior has been underexplored. This paper fills this gap by exploring the impact of China's air quality monitoring and disclosure program on the green innovation of firms; via this program, real-time air quality monitoring stations have been constructed nationwide, and the information is promptly released to the public. The results of a difference-in-differences estimation show that the information program has significantly increased the green innovation of firms, especially the number of green invention patent applications. Moreover, this increase is realized via the regulatory effect of both the local government and public attention. Additionally, the green innovation effect performs stronger in state-owned enterprises (SOEs) and firms in heavily polluting industries. This paper contributes to the related literature from the perspective of central government-led real-time transparent environmental information disclosure. The findings of this study highlight policy implications from the perspective of the importance of the application of information technology in environmental regulation, as well as the vital role of information-based instruments in exerting a public regulatory effect on environmental governance.

Pollution or innovation? How enterprises react to air pollution under perfect information

From 2013 to 2015, China launched a nationwide real-time air quality monitoring and disclosure program in three waves. Since then, the air quality of prefecture-level cities nationwide has been monitored and released publicly in real-time. Using this information program as a natural experiment, we investigate how the response of enterprises' innovation to air pollution changes before versus after this information program. Based on a sample of Chinese listed companies in 289 prefecture-level cities from 2011 to 2016, the results show that the information program encourages enterprise innovation to react to air pollution. However, different types of enterprises have heterogeneity in response to this program. In the mechanism analysis, this paper finds that the information program stimulates enterprises' innovation through improving local governments' environmental protection motivation and promoting public environmental engagement. Our research highlights the critical role of information in the theory and practice of environmental regulation that has long been ignored.

Analysis of Air Pollution Data in India between 2015 and 2019

India suffers from among the worst air pollution in the world. In response, a large government effort to increase air quality monitoring is underway. We present the first comprehensive analysis of government air quality observations from 2015–2019 for PM10, PM2.5, SO2, NO2 and O3 from the Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring (CAAQM) network and the manual National Air Quality Monitoring Program (NAMP), as well as PM2.5 from the US Air-Now network. We address inconsistencies and data gaps in datasets using a rigorous procedure to ensure data representativeness. We find particulate pollution dominates the pollution mix across India with virtually all sites in northern India (divided at 23.5°N) exceeding the annual average PM10 and PM2.5 residential national ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India exceeding the PM10 standard by 50% and the PM2.5 standard by 40%. Annual average SO2, NO2 and MDA8 O3 generally meet the residential NAAQS across India. Northern India has (~10%–130%) higher concentrations of all pollutants than southern India, with only SO2 having similar concentrations. Although inter-annual variability exists, we found no significant trend of these pollutants over the five-year period. In the five cities with Air-Now PM2.5 measurements - Delhi, Kolkata, Mumbai, Hyderabad and Chennai, there is reasonable agreement with CPCB data. The PM2.5 CPCB CAAQM data compares well with satellite derived annual surface PM2.5 concentrations (Hammer et al., 2020), with the exception of the western desert region prior to 2018 when surface measurements exceeded satellite retrievals. Our reanalyzed dataset is useful for evaluation of Indian air quality from satellite data, atmospheric models, and low-cost sensors. Our dataset also provides a baseline to evaluate the future success of National Clean Air Programme as well as aids in assessment of existing and future air pollution mitigation policies.

Exposure modelling for air pollution in India: Challenges and opportunities

BACKGROUND AND AIM: Our consortium of exposure scientists and health researchers –CHAIR-India is developing a nation-wide exposure model for particulate matter. Our aim is to build a comprehensive database which will help develop/validate a hybrid exposure model. In this process, we attempt to discuss the gaps and indicate potential solutions to improve air pollution monitoring data quality. METHODS: Data was collected from online monitors from Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring Stations (CAAQMS) as well as offline monitors as part of the National Ambient Air Quality Monitoring Programme (NAMP) between the years of 2005-2020. Variables assessed were unique station name/ID, dates, pollutant variables – (PM2.5, PM10), geographical information (lat/long) and type of PM data generated (Gravimetric/BAMS). These data were collated, sorted and assessed for consistency and validity. RESULTS:While data from online stations (BAMS) have a high level of completeness in recent years (2015 onwards), older data is sporadic and mostly available for Delhi and Maharashtra. The NAMP station data far outnumbers the CAAQMS data in size and spread, particularly for the years before 2015, but presented greater challenges with completeness and accuracy, especially with the geographical metadata. CONCLUSIONS:The broad reach of CAAQMS and online stations in recent years make them an attractive option for air quality research post 2015 with important opportunities to expand the evidence base for India on health effects of air pollution. Comprehensive nationwide and smaller regional studies will need to rely on data from NAMP to supplement the gaps in data from the online stations. Building capacities of state /regional agencies to enhance the quality and accuracy of data will provide impetus to a growing body of air pollution researchers in India. The CHAIR consortium will complement these efforts by developing an online platform with accurate metadata for all stations and sources for public access. KEYWORDS: India, Air Pollution, Monitoring, Data Quality, Research

Monitoring Air Quality with Transplanted Bryophytes in a Neotropical Andean City.

Air pollution is one of the main global environmental problems, where bryophytes, due to their high capacity to retain metals and other pollutants, have been widely used in active air quality monitoring studies in temperate and tropical zones. Thus, in this study, we analyzed for the first time the concentrations of eight metals (cadmium, copper, nickel, aluminum, iron, manganese, lead and zinc) in three species of transplanted mosses (Rhacocarpus purpurascens (Brid.) Paris, Sphagnum sp. and Thuidium delicatulum (Hedw.) Schimp.) from Ecuador. Significant differences were found for the three species in the concentrations of Al, Mn, Fe and Zn between urban and control areas, pointing to the Central zone as the main source of contamination with the highest concentrations of Al, Fe, Mn and Zn, related to vehicular traffic. Lead did not differ between zones for Rhacocarpus purpurascens and Sphagnum sp.; however, Thuidium delicatulum accumulated different concentrations between urban areas and the control areas. The three species of mosses provided valuable information on the contamination of Al, Fe, Mn, Pb and Zn in the urban area of the city of Loja, and therefore can be used in future air quality monitoring programs over time in tropical cities.

Assessment of Water Quality Along the Southeast Coast of India During COVID-19 Lockdown

The COVID-19 pandemic has affected the entire world and has had a devastating impact on both lives and livelihoods in India. The only way to defeat the rapid spread of COVID-19, is to shut down socio-economic activities and to maintain minimal human interaction with the implementation of a lockdown. Such lockdowns have manifested in a pollution curtailment in almost all spheres of the planet, including in marine pollution. Quantifying this decrease in pollution levels enables the scientific community to assess the contribution of anthropogenic (especially non-essential) activities to global/regional pollution levels. This paper aims to study the impact of the stringent lockdown period (phase 1 and 2) on coastal water quality along the Chennai coast of India, by analyzing suspended matter concentration (SPM), a key element of water quality and diffuse attenuation coefficient, Kd(490), using LANDSAT-8 Operational Land Imager (OLI) data. LANDSAT-8/OLI, L1TP scenes were subjected to radiometric calibration and atmospheric correction to derive surface reflectance values from raw digital numbers using ACOLITE software and a brief insight has been given for the Dark Spectrum Fitting algorithm used in ACOLITE. SPM concentration decreased by 15.48 and 37.50% in the Chennai and Ennore ports, respectively, due to minimal vessel movement and cargo handling. The stringent lockdown led to the operation of fewer thermal plant units, thus less fly ash was emanated, resulting in a 28.05% reduction in SPM levels over Ennore creek. As industrial and commercial activities subsided, the city’s water bodies became clearer than they were just a fortnight prior to the lockdown, with a reduction of 22.26% of SPM in Adyar and 33.97% in Cooum riverine estuaries. Decrease in Kd(490) showed a positive relationship with SPM and thus improved coastal water quality because of the reduction of SPM during this period. The variations in PM2.5 and PM10 concentrations were studied using National Air Quality Monitoring Programme (NAMP) data and reduced levels in particulate matter concentration (PM2.5 and PM10) for the Adyar residential area (24.38 and 28.43%) and for the Nungampakkam commercial area (36.09 and 67.18%) were observed. A significant reduction in PM2.5 concentration (45.63%) was observed in the Ennore-Manali Industrial region.

A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC

Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.