Evaluation of air quality and its variation due to COVID-19 lockdown in Hyderabad, Telangana, India

Air pollution in India is a serious health issue. A countrywide lockdown was imposed in India in response to the COVID-19 pandemic, firstly for three weeks starting from March 24 to April 14, 2020, and then extended until May 3, 2020. Because of the restrictions imposed, pollution levels in cities all over the country have dropped dramatically in just a few days, raised questions among scientists about lockdown as the most effective alternative approach for reducing air pollution. Hyderabad was chosen for this study because it is India's 5th largest city by area and 4th largest city by population, as well as major industrial centre in South-East Asia with strong air quality statistics. In light of the recent COVID-19 outbreak around the country, a detailed analysis based on air quality parameters from six distinct air quality monitoring sites in Hyderabad, Telangana, has been performed. For simple interpretation of air quality data, establishing a correlation between different pollutants, identifying sources of pollution, and determining the most significant parameters, different multivariate statistical approaches such as Cluster analysis (CA), Principle component analysis (PCA), correlation analysis, and multiple linear regression analysis (MLR) were used. The aim of this study is to evaluate the major air pollution sources in Hyderabad and to identify the most significant air pollutants based on their individual contributions to the Air Quality Index (AQI). Variation in air quality parameters collected for six air quality monitoring stations were represented using box or whisker plots. The data set has been grouped into four major clusters depending on the similarities in the air quality data. Major sources of air pollution in each cluster were identified using PCA. MLR analysis was used to create models for predicting AQI for each cluster based on concentrations of important air contaminants. The findings revealed that PM10 and PM2.5 play a significant role in determining AQI levels. © 2023 Author(s).

Assessment of changes in air quality in Bangalore, India, during the Covid-19 pandemic lockdown: Statistical modelling

COVID-19 pandemic has resulted in a halt to the daily lifestyle of people around the world and bound them to abide by the lockdown measures enforced to prevent the disease from further spreading. In India also, lockdown has been enforced from March 2020. As a result, the level of air pollutants in the atmosphere goes on decreasing. To know the air quality pattern of Bangalore city, ten stations around the city were selected. Air quality data of these stations has been availed from the Central Pollution Control Board (CPCB) of India website. Box chart concept of graphical representation has been applied to show the range of temporal variation of the air pollutants selected (CO, NO2, Ozone, PM2.5, PM10 and SO2) for the study area over two distinct periods (pre-lockdown and post-lockdown). It has been observed that all the pollutants level were drastically or significantly reduced except for SO2 which showed mixed behavior during the entire study period probably due to no restriction on the operation of power plants. GIS based contour mapping is done for each pollutant over the entire study area and separately for two distinct periods (pre-lockdown and post-lockdown). It was found that, change in CO level over the entire study area was significant and the reason behind it was complete restriction on vehicular movement which is the primary reason for CO emission in atmosphere. Reduction in PMs and ozone was also noticeable, but change in SO2 over the entire study area was almost insignificant. To find out the probable sources of pollution during the lockdown and before the lockdown period and the most significant parameters statistical approach has been adopted. The whole data set has been grouped based on similarity and divided into three distinct clusters for both pre-lockdown and post-lockdown period separately using Hierarchical Agglomerative Cluster Analysis (HACA) concept. Principal Component Analysis (PCA) was done for each of the clusters and each time period considered. From the results of PCA it can be confirmed that the most significant parameters were PM10, PM2.5, ozone and SO2. Results suggest that the probable sources of pollution during pre-lockdown period were vehicular emissions, power plants, industrial activities etc. In contrast, during post-lockdown period the sources of pollution were power plants, construction sites and household pollution only. MLR (Multiple Linear Regression) models were developed to predict Air Quality Index (AQI). Most of the models showed good fit with adjusted R2 value more than 0.9. Regression coefficient (R2) values for PM10 followed PM2.5 were highest in each cluster. © 2023 Author(s).

Association between measured air pollution levels in the troposphere and surface air with COVID-19 hospitalization in Tomsk, Russia, 2022

The results of a preliminary analysis of the relationship between the short-term impact of air pollution exposure on hospitalizations associated with COVID-19 in Tomsk, Russia are presented. The statistical data on air pollution and COVID-19 associated hospitalization were collected and analyzed for the period from March 16, 2022 to April 14, 2022. This period corresponds to a flat plateau of confirmed COVID-19 cases after the main pandemic wave in 2022 in Tomsk and the Tomsk region which were associated with omicron strain of SARS-CoV-2. It was found that all representative peaks in a graph of daily hospitalizations coincide with the peaks in graphs of measured levels of air pollution. The increase in hospitalizations occurred on the same days when air pollution levels increased, or with a slight lag of 1-2 days. This allows us to tentatively conclude that air pollution has a quick effect on infected persons and may provoke an increase in symptoms and severity of the disease. Further detailed research is required. © 2022 SPIE.

A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown.

Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R2 values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.

Critical air pollutant assessments and health effects attributed to PM2.5 during and after COVID-19 lockdowns in Iran: application of AirQ+ models.

Objectives: The aim of this study was to evaluate changes in air quality index (AQI) values before, during, and after lockdown, as well as to evaluate the number of hospitalizations due to respiratory and cardiovascular diseases attributed to atmospheric PM2.5 pollution in Semnan, Iran in the period from 2019 to 2021 during the COVID-19 pandemic. Methods: Daily air quality records were obtained from the global air quality index project and the US Environmental Protection Administration (EPA). In this research, the AirQ+ model was used to quantify health consequences attributed to particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5). Results: The results of this study showed positive correlations between air pollution levels and reductions in pollutant levels during and after the lockdown. PM2.5 was the critical pollutant for most days of the year, as its AQI was the highest among the four investigated pollutants on most days. Mortality rates from chronic obstructive pulmonary disease (COPD) attributed to PM2.5 in 2019-2021 were 25.18% in 2019, 22.55% in 2020, and 22.12% in 2021. Mortality rates and hospital admissions due to cardiovascular and respiratory diseases decreased during the lockdown. The results showed a significant decrease in the percentage of days with unhealthy air quality in short-term lockdowns in Semnan, Iran with moderate air pollution. Natural mortality (due to all-natural causes) and other mortalities related to COPD, ischemic heart disease (IHD), lung cancer (LC), and stroke attributed to PM2.5 in 2019-2021 decreased. Conclusion: Our results support the general finding that anthropogenic activities cause significant health threats, which were paradoxically revealed during a global health crisis/challenge.

Air pollution and its associated health risks before and after COVID-19 in Shaanxi Province, China.

Air pollution is a serious environmental problem that damages public health. In the present study, we used the segmentation function to improve the health risk-based air quality index (HAQI) and named it new HAQI (NHAQI). To investigate the spatiotemporal distribution characteristics of air pollutants and the associated health risks in Shaanxi Province before (Period I, 2015-2019) and after (Period II, 2020-2021) COVID-19. The six criteria pollutants were analyzed between January 1, 2015, and December 31, 2021, using the air quality index (AQI), aggregate AQI (AAQI), and NHAQI. The results showed that compared with AAQI and NHAQI, AQI underestimated the combined effects of multiple pollutants. The average concentrations of the six criteria pollutants were lower in Period II than in Period I due to reductions in anthropogenic emissions, with the concentrations of PM2.5 (particulate matter ≤2.5 µm diameter), PM10 (PM ≤ 10 µm diameter) SO2, NO2, O3, and CO decreased by 23.5%, 22.5%, 45.7%, 17.6%, 2.9%, and 41.6%, respectively. In Period II, the excess risk and the number of air pollution-related deaths decreased considerably by 46.5% and 49%, respectively. The cumulative population distribution estimated using the NHAQI revealed that 61% of the total number of individuals in Shaanxi Province were exposed to unhealthy air during Period I, whereas this proportion decreased to 16% during Period II. Although overall air quality exhibited substantial improvements, the associated health risks in winter remained high.

Machine Learning Approaches Used for Air Quality Forecast: A Review

Air Quality Index (AQI) is an indicator of the pollution level of our surroundings and household. Prediction of the AQI values from the historical values can help us analyze and mitigate the pollution levels. The AQI values can be classified into predetermined categories and machine learning algorithms can be made use of to improve the classification accuracy of the Air Quality Index value calculated. The main objective of the paper is to provide the potential researchers, with the importance of various Machine Learning approaches used for the forecast of the Air Quality Index. This paper analyzes various strategies used for the prediction, classification of AQI incorporating machine learning techniques. The air quality index can be calculated using Machine learning-based methods. Some of the methods to be considered are logistic regression, decision tree, support vector regression, support vector classifier, random forest tree, Naive Bayes classifier, and K-nearest neighbor. Application of these methods on the Air Quality Index datasets may yield different Accuracy, Recall, and F1 Score. Different algorithms that can be used for the said purpose with their strengths are summarized in a comparison table.

The implication of pollution control acts & protocols and COVID-19 on air quality: A case study of Mumbai, India

This study aims to explore twin objectives. Initially, the study scrutinises the consequences of various pollution control acts and protocols signed by India to improve the air quality and then the study involves itself to investigate the aftermath of COVID-19 lockdown on the air quality of highly populated Mumbai city of India. The empirical analysis is facilitated by the application of Poirier's Spline function approach on the secondary data compiled from the Maharashtra Pollution Control Board (MPCB). The corresponding structural shifting points are identified through the CUSUM of squares (CUSUMQ) test. The empirical results disclose that Kyoto Protocol and lockdown have positively influenced the air quality. This study ends with suitable policy prescriptions. © 2023 by Shrabanti Maity, Ummey Rummana Barlaskar and Nandini Ghosh.

Exposures to Particles and Volatile Organic Compounds across Multiple Transportation Modes

Travellers may be exposed to a wide range of different air pollutants during their journeys. In this study, personal exposures within vehicles and during active travel were tested in real-world conditions across nine different transport modes on journeys from London Paddington to Oxford City Centre, in the United Kingdom. The modes tested covered cycling, walking, buses, coaches, trains and private cars. Such exposures are relevant to questions of traveller comfort and safety in the context of airborne diseases such as COVID-19 and a growing awareness of the health, safety and productivity effects of interior air quality. Pollutants measured were particle number (PN), particle mass (PM), carbon dioxide (CO2) and speciated volatile organic compounds (VOCs), using devices carried on or with the traveller, with pumped sampling. Whilst only a relatively small number of journeys were assessed—inviting future work to assess their statistical significance—the current study highlights where a particular focus on exposure reduction should be placed. Real-time results showed that exposures were dominated by short-term spikes in ambient concentrations, such as when standing on a train platform, or at the roadside. The size distribution of particles varied significantly according to the situation. On average, the coach created the highest exposures overall;trains had mixed performance, while private cars and active transport typically had the lowest exposures. Sources of pollutants included both combustion products entering the vehicle and personal care products from other passengers, which were judged from desk research on the most likely source of each individual compound. Although more exposed to exhaust emissions while walking or cycling, the active traveller had the benefit of rapid dilution of these pollutants in the open air. An important variable in determining total exposure was the journey length, where the speed of the private car was advantageous compared to the relative slowness of the coach. © 2023 by the authors.

Air pollution assessment in Seoul, South Korea, using an updated daily air quality index

The variability of daily air quality index DAQx* was analyzed for types of air quality monitoring stations (AQMSs) in Seoul, South Korea, from 2018 to 2021. Daily maximum 1-h means of O3 and NO2 and daily 24-h means of PM10 and PM2.5 from 42 AQMSs were used to calculate the DAQx*. The frequencies of DAQx* values in DAQx* classes 3 (satisfactory) and 4 (sufficient) dominated for all station types, followed by DAQx* class 5 (poor). The variability of DAQx* values within station types mostly corresponded approximately to one DAQx* class, with mean frequencies of 82% for roadside, 81% for urban, and 72% for background stations. Lower air pollution levels on weekends than weekdays were shown for roadside stations by frequencies of DAQx* values in classes 3 (11% higher) and 4 (12% lower) during summer. NO2 was the air pollutant that annually most frequently formed DAQx* at roadside (48%) and urban (32%) stations, while O3 was the dominant pollutant (38%) at background stations. In winter, PM10 was the most common contributor to the DAQx* (at least 47%) at all station types. The dominant air pollutants in summer were NO2 at roadside stations (72%) and O3 at urban (63%) and background (68%) stations. Air quality improvement during a stronger social distancing period in 2020 due to the COVID-19 pandemic was evidenced by higher frequencies in DAQx* class 3 (up to 26%) but lower frequencies in DAQx* class 4 (up to 24%) than that during the reference period, especially for the roadside stations. © 2023 Turkish National Committee for Air Pollution Research and Control

Associated impact of the COVID-19 induced lockdown on air quality of Kolkata Metropolitan Area (India)

As a pandemic, COVID 19 spread worldwide in early 2020. Primarily densely populated countries had remained vulnerable due to this biological hazard. Many people were forced to stay home owing to nature of the disease and no respite. A nationwide lockdown was implemented in India for 29 days (March 24th to April 21st) of 2020 during the wake of the COVID-19 pandemic. During the nationwide lockdown, industries, transport, and other commercial activities were suspended, except for necessary services. During the entire pandemic situation, an affirmative impact was observed as the air quality was reported to have improved worldwide. The complete economic lockdown to check COVID-19, brought unforeseen relief from severe condition of air quality. An apparent, reduction in level of PM2.5 and Air Quality Index (AQI) was experienced over Mumbai, Delhi, Kolkata, Hyderabad, and Chennai. Present work explores the various metrics of air pollution in Kolkata, West Bengal, India (imposed as a result of containment measure for COVID-19). The polluting parameters (e.g., PM10, PM2.5, SO2, NO2, CO, O3, and NH3) were chosen for seven monitoring stations (Ballygunge, Fort William, Victoria, Bidhannagar, Jadavpur, Rabindra Bharati, Rabindra Sarabar), which are spread across the metropolitan area of Kolkata. National Air Quality Index (NAQI) has been used to show pre-and during-lockdown air quality spatial patterns. The findings showed major changes in air quality throughout the lockdown period. The highest reduction in pollutants emission was observed for: PM10 (- 60.82%), PM2.5 (-45.05%) and NO2 (-62.27%), followed by NH3 (- 32.12%) and SO2 (-32.00%), CO (-47.46%), O3 (15.10%). During the lockdown, the NAQI value was reduced by 52.93% in the study area. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.

Weather indicators and improving air quality in association with COVID-19 pandemic in India.

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.

Comprehensive analysis of ambient air quality during second lockdown in national capital territory of Delhi.

The lockdown imposed in Delhi, due to the second wave of the COVID-19 pandemic has led to significant gains in air quality. Under the lockdown, restrictions were imposed on movement of people, operation of industrial establishments and hospitality sector amongst others. In the study, Air Quality Index and concentration trends of six pollutants, i.e. PM2.5, PM10, NO2, SO2, CO, and O3 were analysed for National Capital Territory of Delhi, India for three periods in 2021 (pre-lockdown: 15 March to 16 April 2021, lockdown: 17 April to 31 May 2021 and post-lockdown: 01 June to 30 June). Data for corresponding periods in 2018-2020 was also analysed. Lockdown period saw 6 days in satisfactory AQI category as against 0 days in the same category during the pre-lockdown period. Average PM2.5, PM10, NO2 and SO2 concentrations reduced by 22%, 31%, 25% and 28% respectively during lockdown phase as compared to pre-lockdown phase, while O3 was seen to increase. Variation in meteorological parameters and correlation of pollutants has also been examined. The significant improvement arising due to curtailment of certain activities in the lockdown period indicates the importance of local emission control, and helps improve the understanding of the dynamics of air pollution, thus highlighting policy areas to regulatory bodies for effective control of air pollution.

Important revelations of different degrees of COVID-19 lockdown on improving regional air quality: a case study of Shijiazhuang, China.

To control the spread of COVID-19, Shijiazhuang implemented two lockdowns of different magnitudes in 2020 (lockdown I) and 2021 (lockdown II). We analyzed the changes in air quality index (AQI), PM2.5, O3, and VOCs during the two lockdowns and the same period in 2019 and quantified the effects of anthropogenic sources during the lockdowns. The results show that AQI decreased by 13.2% and 32.4%, and PM2.5 concentrations decreased by 12.9% and 42.4% during lockdown I and lockdown II, respectively, due to the decrease in urban traffic mobility and industrial activity levels. However, the sudden and unreasonable emission reductions led to an increase in O3 concentrations by 160.6% and 108.4%, respectively, during the lockdown period. To explore the causes of the O3 surge, the major precursors NOx and VOCs were studied separately, and the main VOCs species affecting ozone formation during the lockdown period and the source variation of VOCs were identified, and it is important to note that the relationship between diurnal variation characteristics of VOCs and cooking became apparent during the lockdown period. These findings suggest that regional air quality can be improved by limiting production, but attention should be paid to the surge of O3 caused by unreasonable emission reductions, clarifying the control priorities for urban O3 management.

Consequences of pre and post confinement on the atmospheric air pollutants during spread of COVID-19 in India.

COVID-19, a severe respiratory syndrome, was diagnosed in Wuhan, China, and in the last week of January 2020, it was reported in India. The drastic speed of spreading of COVID-19 imposed a total lockdown in India for the first time in four stages. This leads to restrictions on transport, industries, coal-based power plants, etc. During these stages of lockdown, a detailed analysis was done to study the effect of confinement on various air pollutants, PM10, PM2.5, SO2, CO, NH3, and NOx (NO, NO2) over the thirteen different stations situated at different states in India. The data were compared with pre-confinement duration at different locations in India. During confinement, the air pollutants showed less value when compared with the pre-confinement stage alarming everyone and also the Indian government to bring up rules and regulations for better air quality index so that such pandemics should be reduced.

Predictive modeling and analysis of air quality - Visualizing before and during COVID-19 scenarios.

Quality air to breathe is the basic necessity for an individual and in recent times, emission from various sources caused by human activities has resulted in substantial degradation in the air quality. This work focuses to study the inadvertent effect of COVID-19 lockdown on air pollution. Pollutants' concentration before- and during- COVID-19 lockdown is captured to understand the variation in air quality. Firstly, multi-pollutant profiling using hierarchical cluster analysis of pollutants' concentration is performed that highlights the differences in the cluster compositions between before- and during-lockdown time periods. Results show that the particulate matter (PM10 and PM2.5) in air that formed the primary cluster before lock-down, came down to close similarity with other clusters during lockdown. Secondly, predicting air quality index (AQI) based on the forecasts of pollutants' concentration is performed using neural networks, support vector machine, decision tree, random forest, and boosting algorithms. The best-fitted models representing AQI is identified separately for before- and during-lockdown time periods based on its predictive power. While deterministic method reactively evaluates present AQI when current pollutants' concentration at a particular time and place are known, this study uses the best fitted data-driven model to determine future AQIs based on the forecasts of pollutant's concentration accurately (overall RMSE<0.1 for before lockdown scenario and <0.3 for during lockdown scenario). The study contributes to visualize the variation in pollutants' concentrations between the two scenarios. The results show that the reduced economic activities during lockdown period had led to the drop in concentration of PM10 and PM2.5 by 27% and 50% on an average. The findings of this study have practical and societal implications and serve as a reference mechanism for policymakers and governing bodies to revise their actions plans for regulating individual air pollutants in the atmospheric air.

Indoor Air Quality Assessment of Metro Stations Based on the Optimal Combination Weight and Improved Fuzzy Comprehensive Evaluation

Aair quality issues and respiratory diseases have become issues of particular concern since the outbreak of the COVID-19 pandemic. The indoor air quality of crowded places such as underground metro stations has received growing attention from passengers and staff, thus requiring both qualitative and quantitative assessment. However, the traditional fuzzy comprehensive evaluation is ineffective in this respect. Therefore, this paper proposed the method of optimal combination weight and improved fuzzy comprehensive evaluation to assess the air quality. First, subjective weights were calculated with the multiple-input weighted precedence chart and analytic hierarchy process;objective weights were computed using the entropy weight and exceedance multiple methods. Second, the moment estimation theory was introduced for the optimal combination of these weights. Results show that the optimal combination weighting method achieves the minimum relative deviation. Moreover, in the traditional fuzzy comprehensive evaluation, the air quality is generally classified based on the maximum membership, and the evaluation is inapplicable when the validity (K0) is less than 0.5. Therefore, the concept of confidence was introduced herein for improvement. Finally, the optimal combination weight and improved fuzzy comprehensive evaluation is proved to be the most reasonable in comparison with the traditional fuzzy comprehensive evaluation and indoor air quality index. This study not only suggests a good method to assess the indoor air quality of metro stations but also provides references for decision makers.

Assessment of air quality index and pollutants in Chennai city, India: Pre-Covid and during- Covid period

The lockdown was implemented by the government of India during the pandemic period due to Covid-19. This paper presents the effect of lockdown on the air quality index and various pollutants in five major locations in the Chennai city of Tamil Nadu. The air pollutants such as PM10, PM2.5, SO2 and NO2 from the monitoring stations were analyzed from 2018 to 2019 (pre-Covid period) and from 2020 to 2021 (during-Covid period). The results demonstrated that the concentration of PM10 and PM2.5 reduced about 48% and 39% respectively. Similarly, significant reduction in the pollutants SO2 (-25%) and NO2 (-10%) has been observed. In the same way, AQI level before and during lockdown in Chennai city was observed satisfactory to moderate categories. The maximum reduction in AQI was observed in Adyar (-50.38%), followed by Nungambakkam (-44.18%), TNagar (-40.31%), Anna Nagar (-39.98%) and Kilpauk (-30.74%). Overall study implies that regulatory measures in a certain location in a suitable time period control the pollution and protects the environment.

Improving Urban Air Quality Monitoring in Delhi, India: Reflections on Low-Cost Air Quality Sensors (LCAQS) and Participatory Engagement

In Delhi, the capital city of India, air pollution has been a perpetual menace to urban sustainability and public health. The present study uses a mixed-method approach to enumerate to the urban authorities: (a) the state of air pollution in the city;(b) systemic flaws in the current monitoring network;(c) potential means to bolster it;and (d) need of a participatory framework for monitoring. Information about Air Quality Index (AQI), obtained from 36 monitoring stations across Delhi is compared between 2021 (20 April–25 May;2nd year/phase of SARS-CoV-2 lockdown), and the corresponding time periods in 2020 (1st year/phase of lockdown), and 2019 (business-as-usual) using the Mann–Whitney U Test. AQI during the 2021 lockdown (a) appeared statistically more similar (p <.01) to that of 2019 and (b) exceeded the environmental health safety benchmark for 85% days during the study period (20 April–25 May). However, this only presented a partial glimpse into the air pollution status. It owes to numerous ‘holes’ in the AQI data record (no data and/or insufficient data). Moreover, certain areas in Delhi yet have no monitoring station, or only too few, to yield a ‘representative’ estimate (inadequate spatial coverage). Such shortcomings in the existing monitoring network may deter future research and targeted/informed decision-making for pollution control. To that end, the present research offers a summary view of Low-Cost Air Quality Sensors (LCAQS), to offer the urban sustainability authorities, ‘complementary’ technique to bolster and diversify the existing network. The main advantages and disadvantages of various LCAQS sensor technologies are highlighted while emphasizing on the challenges around various calibration techniques (linear and non-linear). The final section reflects on the integration of science and technology with social dimensions of air quality monitoring and highlights key requirements for (a) community mobilization and (b) stakeholder engagement to forge a participatory systems’ design for LCAQS deployment. © 2022 National Institute of Urban Affairs.

Intranasal budesonide for rhinitis during a high airborne pollution period: a randomized controlled trial.

BACKGROUND: Air pollution may induce or reinforce nasal inflammation regardless of allergy status. There is limited direct clinical evidence informing the treatment of airborne pollution-related rhinitis. OBJECTIVE: To assess the effectiveness of intranasal budesonide in adults with self-reported rhinitis symptoms triggered/worsened by airborne pollution. METHODS: Adults in northern China with self-reported rhinitis symptoms triggered or worsened by airborne pollution were randomized to budesonide 256 µg/day or placebo for 10 days in pollution season (October 2019 to February 2020). The primary endpoint was the mean change from baseline in 24-h reflective total nasal symptom score (rTNSS) averaged over 10 days. The secondary endpoints were subject-assessed Global Impression of Change (SGIC), mean change from baseline in individual nasal symptom severity, and mean change from baseline in individual non-nasal symptoms of cough and postnasal drip severity. One-sided P < 0.0125 was considered statistically significant. RESULTS: After an interruption by COVID-19, an interim analysis showed that the study could be ended for efficacy with n = 206 participants (103/group) since the primary efficacy endpoint demonstrated significant results. The final efficacy results showed that the 10-day-averaged rTNSS change in the budesonide group was greater than with placebo (- 2.20 vs - 1.72, P = 0.0107). Budesonide also significantly improved 10-day-averaged itching/sneezing change (- 0.75 vs - 0.51, P = 0.0009). Results for SGIC and all other individual symptoms did not show significant differences between the two groups. CONCLUSIONS: Intranasal budesonide 256 µg once daily improved the total nasal symptoms and itching/sneezing over 10 days in adults with rhinitis triggered/worsened by airborne pollution.