Spatio-temporal variation of air quality and environment-economy-urban driving factors: a case study of Hohhot, Inner Mongolia during 2015–2023

The variation in air quality was assessed from the ambient concentrations of various air pollutants [total suspended particle (TSP), particulate matter < or =10 microm (PM(10)), SO(2), and NO(2)] for pre-Diwali, Diwali festival, post-Diwali, and foggy day (October, November, and December), Delhi (India), from 2002 to 2007. The extensive use of fireworks was found to be related to short-term variation in air quality. During the festival, TSP is almost of the same order as compared to the concentration at an industrial site in Delhi in all the years. However, the concentrations of PM(10), SO(2), and NO(2) increased two to six times during the Diwali period when compared to the data reported for an industrial site. Similar trend was observed when the concentrations of pollutants were compared with values obtained for a typical foggy day each year in December. The levels of these pollutants observed during Diwali were found to be higher due to adverse meteorological conditions, i.e., decrease in 24 h average mixing height, temperature, and wind speed. The trend analysis shows that TSP, PM(10), NO(2), and SO(2) concentration increased just before Diwali and reached to a maximum concentration on the day of the festival. The values gradually decreased after the festival. On Diwali day, 24-h values for TSP and PM(10) in all the years from 2002 to 2007 and for NO(2) in 2004 and 2007 were found to be higher than prescribed limits of National Ambient Air Quality Standards and exceptionally high (3.6 times) for PM(10) in 2007. These results indicate that fireworks during the Diwali festival affected the ambient air quality adversely due to emission and accumulation of TSP, PM(10), SO(2), and NO(2).

Air pollution is one of the worst avoidable threats in developing nations across the world. India has undergone a substantial number of infrastructure changes during recent years due to the ever-increasing population. This and the consequent industrialization, the air quality of Indian cities became worsened. The changes in climatic conditions across various cities in India also contribute to air pollution. To control the air pollution within the acceptable limit several control measures have been imposed in India, despite these efforts the air pollution level has not decreased considerably. In India, the first COVID-19 case has reported on 30th January 2020 in the state of Kerala. To control the quick spread of COVID-19 in India, the central government executed a three-week nationwide lockdown from 24th March 2020, and further, it has extended into several phases. It was the first time in India a long-term shutting down of all the sectors happening and which resulted in positively on the environment. This study is dealing with the lockdown effect on air quality in metro cities in India and is compared with the pre-existing conditions. Also, the seasonal variations in air quality in the course of the past two years are compared. The data of pollutants PM10, PM2.5, SO2, NO2, O3, CO, and NH3 from metro cities were collected and by adopting the National Air Quality Index to depict the variations in overall air quality. During the lockdown period, most of the cities experience a considerable improvement in overall air quality and PM10, NO2, PM2.5, and CO concentrations. Whereas, the Ozone shows some increasing trend in a few cities might be due to the increment in the temperature caused by the exposure of sun during the summer season. KeywordsLockdownMetro citiesIndiaAir qualityCOVID-19

Present paper represents the spatio-temporal variation of air quality and performances of geostatistical tools for the identification of pollutants zone in various districts of Assam (India). Geographic Information System (GIS) and geostatistical analysis were utilized to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants. Data of 23 fixed monitoring stations were collected from the Central Pollution Control Board (CPCB). It was observed that SO2 and NOx concentrations are the major pollutants to the deterioration of air quality in Assam State. Exploratory data analysis was considered for the determination of spatial and temporal patterns of air pollutants. Air Quality index (AQI) was calculated based on the air pollutants and particulate matter. Radial Basis Function (RBF) interpolation techniques were used to analyze the spatial and temporal variation of air quality in Assam. Cross-validation is applied to evaluate the accuracy of interpolation methods in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe Equation (NSE) and Accuracy Factor (ACFT). In 2015, the high value of AQI portrayed in the central and northeast of the state. In 2016, the central and entire east of the study area was recorded the highest value of AQI. In 2017, it was observed that mostly the central part of the state recorded the high value of AQI. The spatio-temporal variation trend of air pollutants provides sound scientific basis for its management and control. This information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes. Implications Guwahati is one of the most polluted cities in India provided a novel evidence to find out the impact of air pollution. Present study has been suffered from several limitations, like (i) the daily or weekly concentration of air pollutants was not gained due to limited monitoring technique, (2) dearth of regular information of PM2.5 collection, which were not regularly connected. Present study is used to estimate the spatio-temporal variations (2015–2017) of gaseous and particulate air pollutants using GIS and spatial statistical approach. Probably, this is the first study to report the spatial and temporal variation of air quality distribution in Assam. Results showed there is a negative impact on the ambient air quality status of Assam. These industries and mining areas contribute significantly to the air pollution in this deltaic region. This district-wise information of air pollution congregation would be valuable for urban planners and decision architects to efficiently administer air quality for health and environmental purposes. The dissimilarity in geographical dissemination of the pollutant concentration has been more helpful in seasonal inevitability. Consequently, a continuous set of data and more parameters can be included to attain more reliable results.

The novel coronavirus disease 2019 (COVID-19) has become a serious health concern worldwide for almost a year. This study investigated the effects of selected air pollutants and meteorological variables on daily COVID-19 cases in Dhaka city, Bangladesh. Air pollutants and meteorological data for Dhaka city were collected from 8 April to 16 June 2020 from multiple sources. This study implied spearman’s correlation to see the correlation between daily COVID-19 cases and different air pollutants and meteorological variables. Besides, multiple linear regression and the Generalized Additive Model (GAM) were used to investigate the association between COVID-19 cases and other variables used in this study. Due to lockdown measures, significant differences between PM2.5, SO2, NO2, CO, and O3 in 2019 and 2020 were observed in Dhaka city. We used lag-0, lag-7, lag-14, and lag-21 days on daily COVID-19 cases to look at the lag effect of different air pollutants and meteorology. The LRM results showed that the daily COVID-19 cases are significantly correlated with relative humidity (lag-0 days) and pressure (lag-14 days) (p < 0.05). Additionally, the GAM model results showed a significant nonlinear association among daily COVID-19 cases and meteorology and air quality variables on different lag days. Therefore, our results suggest that an effective public health intervention measures should be implemented to slowdown the spreading of COVID-19.

This work studied the spread of COVID-19, the meteorological conditions and the air quality in a megacity from two viewpoints: (1) the correlation between meteorological and air quality (PM10 and NO2) variables with infections and deaths due COVID-19, and (2) the improvement in air quality. Both analyses were performed for the pandemic lockdown due to COVID-19 in the City of Buenos Aires (CABA), the capital and the largest city in Argentina. Daily data from temperature, rainfall, average relative humidity, wind speed, PM10, NO2, new cases and deaths due COVID-19 were analyzed. Our findings showed a significant correlation of meteorological and air quality variables with COVID-19 cases. The highest temperature correlation occurred before the confirmation day of new cases. PM10 presented the highest correlation within 13 to 15 days lag, while NO2 within 3 to 6 days lag. Also, reductions in PM10 and NO2 were observed. This study shows that exposure to air pollution was significantly correlated with an increased risk of becoming infected and dying due to COVID-19. Thus, these results show that the NO2 and PM10 levels in CABA can serve as one of the indicators to assess vulnerability to COVID-19. In addition, decision-makers can use this information to adopt strategies to restrict human mobility during the COVID-19 pandemic and future outbreaks of similar diseases in CABA.

Roles of pollution in the prevalence and exacerbations of allergic diseases in Asia

Objective: To better understand how meteorological variables, air quality variables, and pollen counts collectively contribute to asthma-related emergency department visits (AREDV) and asthma-related hospitalizations (ARH) among pediatric and adult patients in the New York City borough of the Bronx. Methods: The numbers of daily adult and pediatric AREDV and ARH from 2001 to 2008 were obtained from three Bronx hospitals. After removing outliers, interpolating missing data, and standardizing variable values by scaling the data using z-scores, data were analyzed using Spearman rank tests and linear regression models for the full year and each season. Results: There were a total of 42,065 AREDV and 1,664 ARH at both Bronx hospitals. With the exception of a spring peak in AREDVs, AREDVs and ARHs follow a cyclical pattern, climbing in the fall, plateauing in the winter, dropping in the spring, and reaching a low in the summer. Among the 11 air quality, meteorological, and pollen count variables, temperature and tree pollen made the greatest contribution to AREDV with scaled coefficients of –0.337 and 0.311 respectively; equating to an additional AREDV for every 5.0-unit decrease in temperature and an additional AREDV for every 186.0-unit increase in tree pollen. These two variables were confirmed to have independent associations with AREDV prior to the data interpolation. Grass pollen was also found to have a relatively large contribution to AREDV during the summer with a scaled coefficient of 0.314, equating to an additional AREDV for every 2.3-unit increase in grass pollen. Conclusion: There are distinct peaks of increased AREDVs that are closely associated with increased tree pollen counts in the spring and decreasing temperatures in the fall. Early anticipation of these air quality, meteorological, and pollen factor changes based on ongoing surveillance could potentially guide clinical practice and minimize AREDVs in the Bronx.

The effect of fireworks on air quality was assessed from the ambient concentrations of various air pollutants (SO2, NO2, PM10 and TSP) during Diwali festival in Hisar city (India), in November 1999. The extensive use of fireworks was found to be related to short-term variation in air quality. During the festival the concentration of SO2 was observed to be increased approximately 10-fold at few sites, whereas the concentrations of NO2, PM10 and TSP increased 2-3 times, compared to the data collected on a typical winter day in December 1999. The maximum NO2 concentration was observed a day after the festival. The diurnal pattern of the above pollutants showed a slight increase in the night. The levels of these pollutants observed during Diwali were found to be moderately high, which can be associated with serious health impacts.

In the present times, air quality is a major concern in whole world. Major parts of the world irrespective of developing or developed nations are a victim to Air Pollution; it is one of the biggest problems. Air quality is of significant concern because of its negative effect on the health of the region?s living conditions, climate, and economy it is caused by harmful pollutants released from natural or man - made activities. [1] Such pollutants causing air Pollution are CO2, CO, SO2, NO2, O3, Suspended particulate Matter (SPM), Respirable particulate matter (RSPM), volatile organic compounds such as Alcohols, benzene, etc. and smoke. [2]. Therefore it is a bit challenging but necessary task to monitor air quality in real time looking towards the upcoming time. As a part of experiment monitoring of air Quality in real time using Arduino microcontroller unit with MQ - 135 air quality sensor is done. It measures NH3, NOx, alcohol, Benzene, smoke, CO2, etc. [3] which can be displayed in ppm And DHT11 humidity and temperature sensor which can be displayed in percentage and degree Celsius respectively and plotting of graphs showing variation in air quality with respect to time, temperature, humidity, different locations and seasonal variation in air quality. Also an alarm was connected with Arduino unit so that when air quality goes down and LCD shows value beyond certain permissible limit then alarm will start beeping to alert people about the severity of air. The benefits of sensor, having reliable stability, rapid response Recovery, having long life, affordable as cost and power consumption is minimum, Portable, light in weight, and user friendly. All the data?s have been shown so that a fair variation could be observed in the air quality.

Rising adult asthma prevalence (AAP) rates and asthma emergency room (AER) visits constitute a large burden on public health in Utah (UT), a high-altitude state in the Great Basin Desert, USA. This warrants an investigation of the characteristics of the counties with the highest asthma burden within UT to improve allocation of health resources and for planning. The relations between several predictor environmental, health behavior and socio-economic variables and two health outcome variables, AAP and AER visits, were investigated for UT’s 29 counties. Non-parametric statistical comparison tests, correlation and linear regression analysis were used to determine the factors significantly associated with AER visits and AAP. Regression kriging with Utah small area data (USAD) as well as socio-economic and pollution data enabled local Moran’s I cluster analysis and the investigation of moving correlations between health outcomes and risk factors. Results showed the importance of desert/mining dust and socio-economic status as AAP and AER visits were greatest in the south of the state, highlighting a marked north–south divide in terms of these factors within the state. USAD investigations also showed marked differences in pollution and socio-economic status associated with AAP within the most populous northern counties. Policies and interventions need to address socio-economic inequalities within counties and between the north and south of the state. Fine (PM2.5) and coarse (PM10) particulate matter monitors should be installed in towns in central and southern UT to monitor air quality as these are sparse, but in the summer, air quality can be worse here. Further research into spatiotemporal variation in air quality within UT is needed to inform public health interventions such as expanding clean fuel programs and targeted land-use policies. Efforts are also needed to examine barriers to routine asthma care.

Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling.

Ambient air quality trends and driving factor analysis in Beijing, 1983–2007

The COVID-19 pandemic has significantly changed the air pollution of the world. The present study investigated the temporal and spatial variability in air quality in Xi’an, China, and its relationship with meteorological parameters during and before the COVID-19 pandemic. The outcomes of this study indicated that air pollutants, PM2.5, NO2, PM10, CO, and SO2 are likely to decrease during winter (25%, 50%, 30%, 40%, and 35%) to spring (30%, 55%, 38%, 50%, and 40%) and summer (40%, 58%, 60%, 55%, and 47%), respectively. However, the concentration of O3-8h increased by 40%, 55%, and 65% during winter, spring, and summer, respectively. The values of the air quality index decreased during the COVID-19 period. Furthermore, significant positive trends were reported in PM2.5, NO2, PM10, O3, and SO2, and no notable trends in CO during the COVID-19 pandemic. Both during and before the COVID-19 period, PM10, NO2, PM2.5, CO, and SO2 showed a negative correlation with the temperature and a moderately positive significant correlation between O3-8h and temperature. The findings of this study would help understand the air pollution circumstances in Xi’an before and during the COVID-19 period and offer helpful information regarding the implications of different air pollution control strategies.

During the lamp event in phase-1 (P1) of the first wave of the COVID-19 pandemic, the concentration of air pollutants over the Indo Gangetic Plain (IGP) increased substantially. Analyses show an association of benzene and toluene with PM2.5 due to oil-based emissions.

&amp;lt;p&amp;gt;The degradation of air quality is a challenge that policy-makers face all over the world. According to the World Health Organisation, air pollution causes an estimate of 7 million premature deaths every year. In this context, air quality forecasts are crucial tools for decision- and policy-makers, to achieve data-informed decisions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Global forecasts, such as the Copernicus Atmosphere monitoring service model (CAMS), usually exhibit biases: systematic deviations from observations. Adjusting these biases is typically the first step towards obtaining actionable air quality forecasts. It is especially relevant in health-related decisions, when the metrics of interest depend on specific thresholds.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;AQ (Air quality) - Bias correction was a project funded by the ECMWF Summer of Weather Code (ESOWC) 2021 whose aim is to improve CAMS model forecasts for air quality variables (NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt;, PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;), using as a reference the in-situ observations provided by OpenAQ. The adjustment, based on machine learning methods, was performed over a set of specific interesting locations provided by the ECMWF, for the period June 2019 to March 2021.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The machine learning approach uses three different deep learning based models, and an extra neural network that gathers the output of the three previous models. From the three DL-based models, two of them are independent and follow the same structure built upon the InceptionTime module: they use both meteorological and air quality variables, to exploit the temporal variability and to extract the most meaningful features of the past [t-24h, t-23h, &amp;amp;#8230; t-1h] and future [t, t+1h, &amp;amp;#8230;, t+23h] CAMS predictions. The third model uses the station static attributes (longitude, latitude and elevation), and a multilayer perceptron interacts with the station attributes. The extracted features from these three models are fed into another multilayer perceptron, to predict the upcoming errors with hourly resolution [t, t+1h, &amp;amp;#8230;, t+23h]. As a final step, 5 different initializations are considered, assembling them with equal weights to have a more stable regressor.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Previous to the modelisation, CAMS forecasts of air quality variables were actually biassed independently from the location of interest and the variable (on average: bias&amp;lt;sub&amp;gt;NO2&amp;lt;/sub&amp;gt; = -22.76, bias&amp;lt;sub&amp;gt;O3&amp;lt;/sub&amp;gt; = 44.30, bias&amp;lt;sub&amp;gt;PM2.5&amp;lt;/sub&amp;gt; = 12.70). In addition, the skill of the model, measured by the Pearson correlation, did not reach 0.5 for any of the variables&amp;amp;#8212;with remarkable low values for NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; (on average: pearson&amp;lt;sub&amp;gt;NO2&amp;lt;/sub&amp;gt; = 0.10, pearson&amp;lt;sub&amp;gt;O3&amp;lt;/sub&amp;gt; = 0.14).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;AQ-BiasCorrection modelisation properly corrects these biases. Overall, the number of stations that improve the biases both in train and test sets are: 52 out of 61 (85%) for NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, 62 out of 67 (92%) for O&amp;lt;sub&amp;gt;3,&amp;lt;/sub&amp;gt;&amp;amp;#160;and 80 out of 102 (78%) for PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;. Furthermore, the bias improves with declines of -1.1%, -9.7% and -13.9% for NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; respectively. In addition, there is an increase in the model skill measured through the Pearson correlation, reaching values in the range of 100-400% for the overall improvement of the variable skill.&amp;lt;/p&amp;gt;