Statistical Tools for Air Pollution Assessment: Multivariate and Spatial Analysis Studies in the Madrid Region

Quantifying the impact of lockdown measures on air pollution levels: A comparative study of Bhopal and Adelaide

This research study presents an in-depth comparison of air quality in Bhopal, India, and Adelaide, Australia, focusing on the impact of COVID-19 restrictions. Utilizing air quality data from 2019 to 2022, the research analyzed the concentrations of pollutants like PM2.5, PM10, NO2, and O3, during pre-lockdown, lockdown, and post-lockdown periods. The findings demonstrate a significant reduction in PM2.5and PM10 levels during lockdown in cities such as Delhi and Haryana in India, and various Chinese cities, while also highlighting complex sources of air pollution like bushfires in regions like Sydney, Australia. In contrast, the study revealed nuanced trends in Bhopal and Adelaide, influenced by local geographical, climatic, and anthropogenic factors. Bhopal exhibited a notable decrease in PM10 and PM2.5levels, but inconsistent patterns in NO2 and CO, while Adelaide experienced marginal changes. The study emphasizes the temporary effectiveness of lockdowns and underscores the need for region-specific, sustainable air quality management strategies. Future implications include considerations for regional specificities, broader atmospheric chemistry, and international collaboration. The research provides valuable insights for urban air quality policy formulation, stressing a data-driven, long-term approach.

Are associations of leisure-time physical activity with mortality attenuated by high levels of chronic ambient fine particulate matter (PM2.5) in older adults? A prospective cohort study

BACKGROUND AND PURPOSE

Although leisure-time physical activity (PA) has established health benefits in older adults, it is equivocal if exercising in environments with high levels of PM_2.5 concentrations is equally beneficial for them. To explore the independent and joint associations of ambient PM_2.5 and PA with all-cause mortality among adults aged 60 or older and to assess the modifying effect of age (60-74 years vs. 75+ years) on the joint associations.

METHODS

A prospective cohort study based on the MJ Cohort repeat examinations (2005-2016) and the Taiwan Air Quality Monitoring Network and death registry linkages (2005-2022). We included MJ Cohort participants aged 60 or more at baseline who attended the health check-ups at least twice (n = 21,760). Metabolic equivalent hours per week (MET-h/week) of leisure-time PA were computed. Multivariable adjusted associations were examined using time-varying Cox proportional hazard models.

RESULTS

There were 3539 all-cause deaths over a mean follow-up of 12.81 (SD = 3.67) years. Ambient PM_2.5 and physical inactivity are both independently associated with all-cause mortality. The joint associations of PA and PM_2.5 concentrations with all-cause mortality differed in the young-old (60-74 years) and the older-old (75+ years) (P for interaction = 0.01); Higher levels of long-term PM_2.5 exposures (≥25 μg/m³) had little influence on the associations between PA and mortality in the young-old (HR = 0.68 (0.56-0.83) and HR = 0.72 (0.59-0.88) for participants with 7.5-<15 and 15+ MET-h/week respectively) but eliminated associations between exposure and outcome in the older-old (HR = 0.91 (0.69-01.21) and HR = 1.02 (0.76-1.38) for participants with 7.5-<15 and 15+ MET-h/week).

CONCLUSION

Long-term exposures to higher PM_2.5 concentrations may eliminate the beneficial associations of PA with all-cause mortality among adults aged 75 and over.

Air quality change and public perception during the COVID-19 lockdown in India

This study aims at analyzing the change in air quality following the COVID-19 lockdown in India and its perception by the general public. Air quality data for 100 days recorded at 193 stations throughout India were analyzed between 25th March to 17th May 2020. A nationwide online survey was conducted to obtain public perceptions of air quality improvement (n = 1750). On average, approximately 40% improvement in the air quality index was observed, contributed by a reduction in 40% of PM₁₀, 44% of PM_2.5, 51% of NO₂ and 21% of SO₂. There was a significant difference between the levels of all the pollutants before and after the lockdown (p < 0.05), except ozone. The correlation between PM₁₀ and PM_2.5 with ozone was significant after the lockdown period, indicating that a significant portion of the particulates present in the atmosphere after the lockdown period is secondary. The values of PM_2.5/PM₁₀ were found to be >0.5 in North East states and this observation points to the long-distance transport of PM_2.5 from other places. The survey for public perception showed that 60% of the respondents perceived improvement in air quality. Household emissions were perceived to be a significant source of pollution after the lockdown. An odds ratio (OR) of 17 (95%, CI: 6.42, 47.04) indicated a very high dependence of perception on actual air quality. OR between air quality and health improvement was 5.2 (95%, CI: 2.69, 10.01), indicating significant health improvement due to air quality improvement. Google Trends analysis showed that media did not influence shaping the perception. There was a significant improvement in the actual and perceived air quality in India after the COVID-19-induced lockdown. PM₁₀ levels had the most decisive influence in shaping public perception.

Statistical analysis, source apportionment, and toxicity of particulate- and gaseous-phase PAHs in the urban atmosphere

Introduction

The concentrations of particulate and gaseous Polycyclic Hydrocarbons Carbon (PAHs) were determined in the urban atmosphere of Delhi in different seasons (winter, summer, and monsoon).

Methodology

The samples were collected using instrument air metric (particulate phase) and charcoal tube (gaseous phase) and analyzed through Gas chromatography. The principal component and correlation were used to identify the sources of particulate and gaseous PAHs during different seasons.

Results and discussion

The mean concentration of the sum of total PAHs (TPAHs) for particulate and gaseous phases at all the sites were found to be higher in the winter season (165.14 ± 50.44 ng/m3 and 65.73 ± 16.84 ng/m3) than in the summer season (134.08 ± 35.0 ng/m3 and 43.43 ± 9.59 ng/m3), whereas in the monsoon season the concentration was least (68.15 ± 18.25 ng/m3 and 37.63 1 13.62 ng/m3). The principal component analysis (PCA) results revealed that seasonal variations of PAHs accounted for over 86.9%, 84.5%, and 94.5% for the summer, monsoon, and winter seasons, respectively. The strong and positive correlation coefficients were observed between B(ghi)P and DahA (0.922), B(a)P and IcdP (0.857), and B(a)P and DahA (0.821), which indicated the common source emissions of PAHs. In addition to this, the correlation between Nap and Flu, Flu and Flt, B(a)P, and IcdP showed moderate to high correlation ranging from 0.68 to 0.75 for the particulate phase PAHs. The carcinogenic health risk values for gaseous and particulate phase PAHs at all sites were calculated to be 4.53 × 10-6, 2.36 × 10-5 for children, and 1.22 × 10-5, 6.35 × 10-5 for adults, respectively. The carcinogenic health risk for current results was found to be relatively higher than the prescribed standard of the Central Pollution Control Board, India (1.0 × 10-6).

An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants

Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive distributed lag, and deleted multiple collinear variables to reduce variables, and then applied six intelligent time series models to forecast the concentrations of the top three pollution sources. We collected two air quality datasets from traffic and industrial monitoring stations and weather data to analyze and compare their results. The results show that a random forest based on selected key variables has better classification metrics (accuracy, AUC, recall, precision, and F1). After deleting the collinearity of the independent variables and adding the lag periods using the autoregressive distributed lag model, the intelligent time-series support vector regression was found to have better forecasting performance (RMSE and MAE). Finally, the research results could be used as a reference by all relevant stakeholders and help respond to poor air quality.

Anatomy of the atmospheric emissions from the transport sector in Greece: trends and challenges

Emissions of atmospheric pollutants are well-known for their adverse effects on air quality and public health. Additionally, GHG emissions are responsible for the so called "Radiating Forcing" leading to climate change and degradation of ecosystem services. In this work, we analyze the annual emission trends of various air pollutants, including GHGs, from all 4 sectors of transport (roads, aviation, navigation, and railway) in Greece during the 28-year period between 1990 and 2017, in order to examine the confounding dynamics among external forces, such as the major fiscal recession of 2008, and the GHG/pollutant emissions in the country. The analysis is performed with a suite of statistical tools consisting of bivariate correlation analysis, Mann-Kendall test, Sen's slope estimation, and Joinpoint regression analysis, in order to thoroughly study the trends of emissions. It is found that all transport sectors (except for the railway) show a significant increase in their emissions, despite the fiscal recession of 2008 that temporarily decelerated all aspects of economic activity in the country. Given the major share of transport in GHG emissions (37%) and air pollution in urban centers, it is essential that the road sector adapts to the new challenges, by means of switching to low-emission technologies and electromobilization. The same applies for the navigation and aviation sectors, which are known pillars of the tourist industry in the country.

Investigations on air quality of a critically polluted industrial city using multivariate statistical methods: Way forward for future sustainability

Industrially developed cities affect public health, and can directly cause inconvenience to the nearby societies especially due to their associated air pollution. In this context, the present study was conducted in Jharsuguda district of Odisha state (India), which is a well-known worldwide hub of industrial clusters. The study area is having mainly medium to large scale industries which makes it prone to poor air quality. A total of twelve air pollutants, namely, PM₁₀, PM_2.5, SO₂, NO₂, CO, O₃, NH₃, and heavy metals (Cu, Mn, Ni, Pb, Zn) were monitored during winter season, at the 16 locations of study area. The air quality data was further assessed using multivariate analysis, and the obtained information was presented using histogram plots, box plots, cluster analysis, principal component analysis (PCA), analysis of variance (ANOVA) analysis, and air quality index (AQI). The statistical analysis results revealed that PM₁₀ and PM_2.5 levels exceeded the permissible limits of study area, ∼40 and 30% of sampling times, respectively. Contrary, values of other pollution parameters were observed to be well within the permissible limits. The cluster analysis distinguishingly summarized the monitoring data into four clusters types, named as severely polluted, moderately polluted, satisfactory, and fine. The PCA analysis of monitored data resulted in identification of prominent emission sources of analyzed pollutants. These sources were mainly found to be associated with coal burning in power plants, agricultural activities, vehicular emissions, and mining activities. The minimum AQI was observed as 87 at Orient (mine no. 4) and Kinjirma which is under satisfactory category, whereas maximum AQI was observed at Bhedabahal with a value of 132 which is under moderate category. Overall, the results of this study indicated that the air pollution of industrial areas must be evaluated thoroughly on regular basis, considering the sustainability of societies and expanding industries.

Spatial distribution of BTEX emission and health risk assessment in the ambient air of pars special economic energy zone (PSEEZ) using passive sampling

Benzene, toluene, ethylbenzene and xylene (BTEX) are a challenging group of volatile organic compounds in industrial and energy areas. Since these aromatics may cause serious diseases such as cancer and respiratory illnesses, they must be monitored. Pars Special Economic Energy Zone (PSEEZ) in Iran is the second largest energy zone of the world with numerous gas refineries and petrochemical complexes for producing a wide range of products. This study is focused on determination of BTEX concentration in the whole South Pars area (46 sampling points) which is the active site of PSEEZ using passive sampling. Then, the results of the passive sampling are used for providing spatial distribution of BTEX using GIS. The annual BTEX measurements revealed that benzene and toluene concentration violates the maximum permitted values at numerous points most of which are located in the vicinity of petrochemical complexes. Active sampling in these complexes not only confirms the results of passive sampling, but also suggests a more intensified BTEX pollution in the air quality of the area which reaches as high as 3500 μg.m^-3 and 18,000 μg.m^-3 for benzene and toluene, respectively, being far beyond the acceptable standards. Health risk analysis also confirms the intensity of BTEX at the selected points. This study suggests a reconsideration of the location of non-operational sites and personnel who are more vulnerable to BTEX contamination. Also, BTEX profile provided by GIS in this research gives a suitable plan for relocating.

No Effects of Meteorological Factors on the SARS-CoV-2 Infection Fatality Rate

OBJECTIVE

Previous studies have shown that meteorological factors may increase COVID-19 mortality, likely due to the increased transmission of the virus. However, this could also be related to an increased infection fatality rate (IFR). We investigated the association between meteorological factors (temperature, humidity, solar irradiance, pressure, wind, precipitation, cloud coverage) and IFR across Spanish provinces ( n = 52) during the first wave of the pandemic (weeks 10-16 of 2020).

METHODS

We estimated IFR as excess deaths (the gap between observed and expected deaths, considering COVID-19-unrelated deaths prevented by lockdown measures) divided by the number of infections (SARS-CoV-2 seropositive individuals plus excess deaths) and conducted Spearman correlations between meteorological factors and IFR across the provinces.

RESULTS

We estimated 2,418,250 infections and 43,237 deaths. The IFR was 0.03% in < 50-year-old, 0.22% in 50-59-year-old, 0.9% in 60-69-year-old, 3.3% in 70-79-year-old, 12.6% in 80-89-year-old, and 26.5% in ≥ 90-year-old. We did not find statistically significant relationships between meteorological factors and adjusted IFR. However, we found strong relationships between low temperature and unadjusted IFR, likely due to Spain's colder provinces' aging population.

CONCLUSION

The association between meteorological factors and adjusted COVID-19 IFR is unclear. Neglecting age differences or ignoring COVID-19-unrelated deaths may severely bias COVID-19 epidemiological analyses.

Is Meteorology a Factor to COVID-19 Spread in a Tropical Climate?

It was speculated that fewer COVID-19 infections may emerge in tropical countries due to their hot climate, but India emerged as one of the leading hotspot. There is no concrete answer on the influence of meteorological parameters on COVID-19 even after more than a year of outbreak. The present study examines the impacts of Meteorological parameters during the summer and monsoon season of 2020, in different Indian mega cities having distinct climate and geography. The results indicate the sign of association, but it varies from one climatic zone to another. The principal component analysis revealed that humidity is strongly correlated with COVID-19 infections in hillocky city Pune (R = 0.70), dry Delhi (R = 0.50) and coastal Mumbai (R = 0.46), but comparatively weak correlation is found in arid climatic city of Ahmedabad. As against the expectations, no discernible correlation is found with temperature in any of the cities. As the virus in 2020 in India largely travelled with droplets, the association with absolute humidity in the dry regions has serious implications. Clarity in understanding the impact of seasonality will greatly help epidemiological research and in making strategies to control the pandemic in India and other tropical countries around the world.

A Review of Metal Levels in Urban Dust, Their Methods of Determination, and Risk Assessment

This review gives insights into the levels of metals in urban dust, their determination methods, and risk assessment. Urban dust harbors a number of pollutants, including heavy metals. There are various methods used for the sampling of urban dust for heavy-metal analysis and source-apportionment purposes, with the predominant one being the use of plastic sampling materials to avoid prior contamination. There are also various methods for the determination of metals, which include: atomic absorption spectroscopy (AAS) and inductively coupled plasma-mass spectrometry (ICP-MS), among others. Studies have shown that pollutants in urban dust are mainly derived from industrial activities and coal combustion, whereas traffic emissions are also an important, but not a predominant source of pollution. The varying particle-size distribution of urban dust and its large surface area makes it easier for the deposition and transport of heavy metals. Risk-assessment studies have shown that metals in urban dust could cause such problems as human pulmonary toxicity and reduction of invertebrate populations. The risk levels seem to be higher in children than adults, as some studies have shown. It is therefore important that studies on metals in urban dust should always incorporate risk assessment as one of the main issues.

An analysis of ozone pollution by using functional data: rural and urban areas of the Community of Madrid

This paper analyses the relationship between tropospheric ozone levels in rural and urban sites in the Spanish Community of Madrid (CM). The paper makes use of functional data and the Kendall's Tau functional statistic (KFT) to identify significant correlations between areas. The results are based on very detailed data, extracted from hourly records from 33 air-quality stations covering de CM during the period 2011-2018, producing more than 95,500 point observations. The results show a large degree of correlation between distant sites, whereas in a number of cases, they reveal very moderate correlations between adjacent sites. This pattern is suggestive of complex recirculation patterns.

Local Analysis of Air Quality Changes in the Community of Madrid before and during the COVID-19 Induced Lockdown

This paper examines the effect of the COVID-19 induced lockdown upon six pollutants, CO, NO, NO2, PM10, PM2.5, and O3, in the Spanish community of Madrid. The paper relies on clustering methods and multiple regression techniques to control for a battery of potential confounding factors. The results show that the nationwide lockdown, decreed on 13 March by the Spanish government, exerted a statistically significant effect upon most pollution indicators. The estimates range from approximately −82% (NO and NO2) to −3% (CO). Reversely, the COVID-19 induced lockdown raised O3 levels by an average of 20%. By using data from 43 stations spread out among the region, the paper provides a local level analysis. This analysis reveals substantial differences across areas and across pollutants. This observation indicates that any successful approach to improve air quality in the region must be multidimensional.

Multiple air pollutant exposure and lung cancer in Tehran, Iran

Lung cancer is the most rapidly increasing malignancy worldwide with an estimated 2.1 million cancer cases in the latest, 2018 World Health Organization (WHO) report. The objective of this study was to investigate the association of air pollution and lung cancer, in Tehran, Iran. Residential area information of the latest registered lung cancer cases that were diagnosed between 2014 and 2016 (N = 1,850) were inquired from the population-based cancer registry of Tehran. Long-term average exposure to PM₁₀, SO₂, NO, NO₂, NO_X, benzene, toluene, ethylbenzene, m-xylene, p-xylene, o-xylene (BTEX), and BTEX in 22 districts of Tehran were estimated using land use regression models. Latent profile analysis (LPA) was used to generate multi-pollutant exposure profiles. Negative binomial regression analysis was used to examine the association between air pollutants and lung cancer incidence. The districts with higher concentrations for all pollutants were mostly in downtown and around the railway station. Districts with a higher concentration for NOx (IRR = 1.05, for each 10 unit increase in air pollutant), benzene (IRR = 3.86), toluene (IRR = 1.50), ethylbenzene (IRR = 5.16), p-xylene (IRR = 9.41), o-xylene (IRR = 7.93), m-xylene (IRR = 2.63) and TBTEX (IRR = 1.21) were significantly associated with higher lung cancer incidence. Districts with a higher multiple air-pollution profile were also associated with more lung cancer incidence (IRR = 1.01). Our study shows a positive association between air pollution and lung cancer incidence. This association was stronger for, respectively, p-xylene, o-xylene, ethylbenzene, benzene, m-xylene and toluene.

New Robust Cross-Variogram Estimators and Approximations of Their Distributions Based on Saddlepoint Techniques

Let Z(s)=(Z1(s),…,Zp(s))t be an isotropic second-order stationary multivariate spatial process. We measure the statistical association between the p random components of Z with the correlation coefficients and measure the spatial dependence with variograms. If two of the Z components are correlated, the spatial information provided by one of them can improve the information of the other. To capture this association, both within components of Z(s) and across s, we use a cross-variogram. Only two robust cross-variogram estimators have been proposed in the literature, both by Lark, and their sample distributions were not obtained. In this paper, we propose new robust cross-variogram estimators, following the location estimation method instead of the scale estimation one considered by Lark, thus extending the results obtained by García-Pérez to the multivariate case. We also obtain accurate approximations for their sample distributions using saddlepoint techniques and assuming a multivariate-scale contaminated normal model. The question of the independence of the transformed variables to avoid the usual dependence of spatial observations is also considered in the paper, linking it with the acceptance of linear variograms and cross-variograms.

Assessing the immediate impact of COVID-19 lockdown on the air quality of Kolkata and Howrah, West Bengal, India

The worldwide spread of COVID-19 caused a nationwide lockdown in India from 24 March 2020 and was further extended up to 3 May 2020 to break off the transmission of novel Coronavirus. The study is designed to assess the changes in air quality from the pre-lockdown period to the during lockdown period in Kolkata and Howrah municipal corporation, West Bengal, India. GIS-based techniques include the spatial and temporal distribution of pollutants using interpolation method, and on the other hand, statistical methods like analysis of variance (ANOVA) was applied to determine the mean differences two phases and correlation matrix helps to understand the changing association of the pollutants in pre- and during lockdown phases. Significant correlations have been found among the pollutants, ANOVA (Two-Way) has shown the significant mean difference of NAQI between the two phases, F(1,611) = 465.723, p < 0.0001; pairwise comparison for Ballygunge has shown the highest mean difference 108.194 at p < 0.0001 significant level between lockdown and pre-lockdown phase. Significant positive correlation has been found between PM_2.5, PM₁₀ (0.99*); PM_2.5, NO₂ (0.81*); PM₁₀, NO₂ (0.81*); CO, NO₂ (0.77*) and some negative correlations have also been found between O₃, NO (- 0.15); O₃ and NH₃ (- 0.36) in the pre-lockdown phase. The reduction amount of mean concentration from the pre-lockdown phase to during lockdown of the main pollutants like PM_2.5, PM₁₀ and NO₂ are ~ 58.71%, ~ 57.92% and ~ 55.23%. Near Rabindra Bharati University constant emission of PM_2.5, 10 and NO₂ have been recorded due to the nearby Cossipore thermal power station.

Deposition processes over complex topographies: Experimental data meets atmospheric modeling

The present paper describes the assessment of the atmospheric deposition processes in a basin valley through a multidisciplinary approach based on the data collected within an extensive physico-chemical characterization of the soils, combined with the local meteorology. Surface soil cores were collected on a NNW-SSE transect across the Terni basin (Central Italy), between the Monti Martani and the Monti Sabini chains (956 m a.s.l.), featuring the heavily polluted urban and industrial enclave of Terni on its bottom. Airborne radiotracers, namely ²¹⁰Pb and ¹³⁷Cs, have been used to highlight atmospheric deposition. We observed an increased deposition flux of ²¹⁰Pb and ¹³⁷Cs at sites located at the highest altitudes, and the associated concentration profiles in soil allowed to evaluate the role of atmospheric deposition. We also obtained a comprehensive dataset of stable anthropogenic pollutants of atmospheric origin that showed heterogeneity along the transect. The behavior has been explained by the local characteristic of the soil, by seeder-feeder processes promoted by the atmospheric circulation, and was reconciled with the concentration profile of radiotracers by factor analysis. Finally, the substantial impact of the local industrial activities on soil profiles and the role of the planetary boundary layer has been discussed and supported by simulations employing a Lagrangian dispersion model.

Sustainability Analysis of the M-30 Madrid Tunnels and Madrid Río after 14 years of Service Life

In 2007, the excavation of the M-30 ring road located in Madrid and the creation of a green corridor either side of the Manzanares river brought significant change to the metropolitan area. The corridor and linear park which it provided were designed to contribute to the regeneration of the fluvial ecosystem, establish links among residents on each side of the river and promote cultural and leisure activities. This paper provides a sustainability analysis of the excavation of the M-30 (involving the socio-economic and environmental impact) 14 years after its construction. In order to show such an impact, an analysis of the area both prior to the project and after completion, as well as a hypothetical solution that uses improved materials, has been performed. This entails use of the multi-criteria decision-making model named MIVES (initials in Spanish, modelo integrado de valor para una evaluación sostenible). The MIVES method is based on the application of value functions of sustainability indicators selected by socio-economic and environmental criteria, chosen by experts. Results from analysis showed that the excavation of the M-30 considerably improved the sustainability of the area (sustainable index 3.43 and 6.26 both before and after the excavation works). However, use of improved materials in contrast with the application of conventional materials slightly improved the sustainability of the work (Sustainability Index 6.26 and 6.74, respectively, of the conventional materials).

High Density Real-Time Air Quality Derived Services from IoT Networks

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices' dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

Temporal and spatial statistical analysis of ambient air quality of Assam (India)

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 NO_x 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.

Air quality, health impacts and burden of disease due to air pollution (PM10, PM2.5, NO2 and O3): Application of AirQ+ model to the Camp de Tarragona County (Catalonia, Spain)

The purpose of this study was to assess the impact to human health of air pollutants, through the integration of different technics: data statistics (spatial and temporal trends), population attributable fraction using AIRQ+ model developed by the WHO, and burden of disease using Disability-Adjusted Life Years (DALYs). The levels of SO₂, NO, NO₂, O₃, H₂S, benzene, PM₁₀, PM_2.5, CO, benzo(a)pyrene and metals, obtained between 2005 and 2017 from the air quality monitoring network across Camp de Tarragona County, were temporally and spatially determined. Health impacts were evaluated using the AIRQ+ model. Finally, the burden of disease was assessed through the calculation of Years of Lost life (YLL) and Years Lost due to Disability (YLD). In general terms, air quality was good according to European quality standards, but it did not fulfil the WHO guidelines, especially for O₃, PM₁₀ and PM_2.5. Several decreasing (NO, NO₂, SO₂, PM₁₀ and benzene) and an increasing (O₃) temporal trend were found. Correlation between unemployment rate and air pollutant levels was found, pointing that the economic crisis (2008-2014) was a factor influencing the air pollutant levels. Reduction of air pollutant levels (PM_2.5) to WHO guidelines in the Camp de Tarragona County would decrease the adult mortality between 23 and 297 cases per year, which means between 0.5 and 7% of all mortality in the area. In this County, for lung cancer, ischemic heart disease, stroke, and chronic obstructive pulmonary disease due to levels of PM_2.5 above the WHO threshold limits, DAYLs were 240 years. This means around 80 DALYs for 100,000 persons every year -between 2005 and 2017. Population attributable fraction (PAF) and burden of disease (DALYs) methodologies are suitable tools for regional and national policymakers, who must take decisions to prevent and to control air pollution and to analyse the cost-effectiveness of interventions.

Legionella and legionellosis in touristic-recreational facilities: Influence of climate factors and geostatistical analysis in Southern Italy (2001-2017)

Legionella is the causative agent of Legionnaires' disease, a flu-like illness normally acquired following inhalation or aspiration of contaminated water aerosols. Our recent studies revealed that climatic parameters can increase the number of reported cases of community-acquired Legionnaires' disease. Here, we evaluated the presence of Legionella in water networks and the distribution of Legionnaires' disease cases associated with touristic-recreational facilities in the Apulia region (southern Italy) during the period 2001-2017 using geostatistical and climatic analyses. Geostatistical analysis data revealed that the area with the highest concentration of Legionella in water systems also had the greatest number of cases of Legionnaires' disease associated with touristic-recreational facilities. Climatic analysis showed that higher daily temperature excursion (difference between maximum and minimum temperature) on the day of sampling was more often associated with Legionella-positive samples than Legionella-negative samples. In addition, our data highlighted an increased risk of Legionnaires' disease with increases in precipitation and average temperature and with decreases in daily temperature excursion (difference between maximum and minimum temperature over the course of 24 h in the days of incubation period of disease) and minimum temperature. Healthcare professionals should be aware of this phenomenon and be particularly vigilant for cases of community-acquired pneumonia during such climatic conditions and among the tourist population. The innovative geo-statistical approach used in this study could be applied in other contexts when evaluating the effects of climatic conditions on the incidence of Legionella infections.