Concentrations Of Various Pollutants Research Articles

Pollutant Concentration Changes During the COVID-19 Lockdown in Barcelona and Surrounding Regions: Modification of Diurnal Cycles and Limited Role of Meteorological Conditions

One of the consequences of the COVID-19 lockdowns has been the modification of the air quality in many cities around the world. This study focuses on the variations in pollutant concentrations and how important meteorological conditions were for those variations in Barcelona and the surrounding area during the 2020 lockdown. Boundary-layer height, wind speed, and precipitation were compared between mid-March and April 2016–2019 (pre-lockdown) and the same period in 2020 (during lockdown). The results show the limited influence of meteorological factors on horizontal and vertical dispersion conditions. Compared with the pre-lockdown period, during lockdown the boundary-layer height slightly increased by between 5% and 9%, mean wind speed was very similar, and the fraction of days with rainfall increased only marginally, from 0.33 to 0.34, even though April 2020 was extremely wet in the study area. Variations in nitrogen dioxide (hbox {NO}_{{2}}), particulate matter with a diameter less than 10 {mu }m (PM10), and ozone (hbox {O}_{{3}}) concentrations over a 10-year period showed a 66% reduction in hbox {NO}_{{2}}, 37% reduction in PM10, and 27% increase in hbox {O}_{{3}} at a traffic station in Barcelona. The differences in the daily concentration cycle between weekends and weekdays were heavily smoothed for all pollutants considered. The afternoon hbox {NO}_{{2}} peak at the traffic station was suppressed compared with the average daily cycle. The analysis of ozone was extended to the regional scale, revealing lower concentrations at rural sites and higher ones in urban zones, especially in Barcelona and the surrounding area. The results presented not only complement previous air quality COVID-19 lockdown studies but also provide insights into the effects of road-traffic reduction.

Three-dimensional spatial modelling of traffic-induced urban air pollution using the Graz Lagrangian model and GIS

Air pollution is a significant global problem that affects climate, human, and ecosystem health. Traffic emissions are a major source of atmospheric pollution in large cities. The aim of this research was to support air quality analysis by spatially modelling traffic-induced air pollution dispersion in urban areas at the street level. The dispersion model called the Graz Lagrangian model (GRAL model) was adapted to determine the NOx concentration level based on traffic, meteorology, buildings, and street configuration data in one of Tehran’s high traffic routes. In this case, meteorological parameters such as wind speed and direction were considered significant factors. Later, using local and general auto-correlation analyses, temporal and spatial variations in the concentration of NOx were measured at different altitudes. The results showed that the average output concentration of NOx pollutants at different altitudes ranges from 64.5 to 426.6 ppb. The resulting Moran index equals to 0.7–0.9 which indicates a high level of positive spatial auto-correlation. The analysis of the local Moran index represents the overcame pollution clusters with high levels of concentration at low to medium heights and the rise in clusters with low pollution at higher heights, while there is no clear clustering in the middle sections. In addition, the study of pollutant concentration variations over time has shown that pollution peaks occur at 07:00–08:00 and 21:00–22:00.

Study of Persistent Haze Pollution in Winter over Jinan (China) Based on Ground-Based and Satellite Observations

A comprehensive study of the formation process of haze events in the Jinan area of China during winter is conducted based on the ground-based and satellite observation data from 1 December 2020 to 12 January 2021. According to variation of pollutant concentrations, two typical types of haze pollution are found in the Jinan area. During the type 1 haze pollution, the PM2.5 concentrations are greater than 75 μgm−3 and less than 115 μgm−3 with a short duration. The haze is mainly caused by local pollutant emissions and the accumulation of pollutants transported from areas around Jinan. By contrast, type 2 haze pollution episodes have a long duration and peak PM2.5 concentrations between 150 μgm−3 and 250 μgm−3, which is considered heavy pollution. Type 2 haze pollution is mainly caused by a mixture of long-range transported dust with locally emitted pollutants. Moreover, the unfavorable meteorological factors such as stable inversion layer, continuous cold high-pressure system, high relative humidity, and low wind speed play an important role in the formation of both types of haze pollution. In addition, there are significant photochemical processes during the haze pollutions. According to satellite data, the AOD in Jinan and surrounding areas is maintained at a high-level during haze pollution. It indicates that the local pollution is often accompanied by regional pollution during haze pollution events. This study reveals the formation process of haze pollution and promotes the study of regional climate change, which can provide guidance to the government in the prevention and control of haze pollution in East China.

The relationship between PM2.5 pollution and aerosol radiative forcing in a heavy industrial city, Taiyuan, in China

Rapid economic development had an important impact on the concentration of air pollutants and the aerosol properties in Taiyuan City, one of China's most important energy and heavy industry bases. Therefore, the long-term measurements of the optical and radiation properties of aerosols and air pollutants monitored by several urban sites in Taiyuan from 2013 to 2019 were analyzed. During the observation period, the multi-year mean concentrations of PM2.5, PM10, SO2, NO2 and CO were 65 ± 7 μg/m3, 129 ± 14 μg/m3, 57 ± 21 μg/m3, 45 ± 6 μg/m3, 1.45 ± 0.31 mg/m3, respectively. The concentrations of various pollutants were highest in winter (PM2.5: 90 μg/m3; PM10: 149 μg/m3; SO2: 109 μg/m3; NO2: 49 μg/m3; CO: 2.03 mg/m3). After the implementation of air pollution control in 2013, the annual mean concentrations of PM2.5, PM10, SO2 and CO in 2019 had been reduced by 28%, 29%, 73% and 47% respectively compared with those in 2013. But aerosol optical depth (AOD) and radiative forcing had no remarkable downtrend. Annual mean of AOD at 500 nm varied from 0.43 to 0.60 with high single scattering albedo (SSA), all above 0.91. Multi-year average radiative forcing at the top of atmosphere (TOA), interior of atmosphere (ATM), and bottom of the atmosphere (SFC) were − 2.68 ± 2.12 W/m2, 45.02 ± 3.14 W/m2, and − 47.46 ± 3.72 W/m2, respectively. And radiative forcing effects were strongest in winter (TOA: -5.74 ± 3.20 W/m2; ATM: 49.15 ± 3.85 W/m2; SFC: -54.83 ± 5.31 W/m2) due to the stronger anthropogenic emissions. The increase in humidity would increase the concentration of particulate matter and the extinction of the atmosphere. The increase of absorbing aerosols and scattering aerosols during the heating period in winter increased the aerosol radiative forcing, which would warm atmosphere and cool surface and then increase the stability of the atmosphere. Pollutants were difficult to diffuse and gradually accumulated thereby, and the radiative forcing effect continued to increase, forming positive feedback and making continuous pollution.

How Does Air Pollution Influence Housing Prices in the Bay Area?

In this paper we examine the effects of localized air pollution measurements on the housing prices in Oakland, CA. With high-resolution air pollution measurements for NO, NO2, and BC, we can assess the ambient air quality on a parcel-by-parcel basis within the study domain. We combine a spatial lag model with an instrumental variable method to consider both the spatial autocorrelation and endogeneity effects between housing prices and air pollution concentrations. To the best of our knowledge, this is the first work in this field that combines both spatial autocorrelation and endogeneity effects in one model with accurate air pollution concentration measurements for each individual parcel. We found a positive spatial autocorrelation with housing prices using Moral’s I (value of 0.276) with the total sample number of 26,386. Somewhat surprisingly, we found a positive relationship between air pollution and housing prices. There are several possible explanations for this finding. Homeowners in high demand, low-stock housing areas, such as our study, may be insensitive to air pollution when the overall ambient air quality is relatively good. It is also possible that under clean air conditions, low variability in pollutant concentrations has little effect on property values. These hypotheses could be verified with more high-resolution air pollution measurements with a diversity of regions.

Haze-day Trends from 2013 to 2020 and Analysis of Spatiotemporal Characteristics of a Haze Process in Ningbo, China

Air quality in Ningbo continues to improve with a constant decrease in the PM2.5 concentration. However, pollution levels occasionally increase during autumn and winter. To understand the regional and seasonal distributions and the interannual variation during haze days, we analyzed the haze monitoring data from 2013 to 2020 and the haze weather process during January 18–19, 2016. Our results showed a downward trend in the major pollutant concentrations. High PM2.5 concentrations persist in certain areas of Ningbo during winter owing to seasonal haze conditions. However, the annual number of haze days has decreased between 2013 and 2020. Regional variations in pollutant concentrations appear mainly in winter, especially in December and January. The observed concentrations were higher in the north and west, and lower in the south and east. The haze process during January 18–19, 2016, occurred within a height of 0–1.5 km, with high PM2.5 concentrations mainly occurring as small and spherical particles. A higher relative humidity, temperature drop, and stable weather assist in accumulating and sinking pollutants, which cause long-term effects and render diffusion difficult. Although recent national initiatives have been effective, the air quality in northern Ningbo requires further improvement during autumn and winter.

Transit pollution exposure monitoring using low-cost wearable sensors

• Wearable low-cost sensors are a viable solution for transport pollution monitoring. • Novel wearable sensor for monitoring personal exposure during commuting. • Our sensor is accurate (R = 0.78) and consistent (R > 0.98). • Our sensor captures variations caused by route, passenger count and sensor location. • Low-cost sensors support new applications and multiple stakeholders. Transit activities are a significant contributor to a person’s daily exposure to pollutants. Currently obtaining accurate information about the personal exposure of a commuter is challenging as existing solutions either have a coarse monitoring resolution that omits subtle variations in pollutant concentrations or are laborious and costly to use. We contribute by systematically analysing the feasibility of using wearable low-cost pollution sensors for capturing the total exposure of commuters. Through extensive experiments carried out in the Helsinki metropolitan region, we demonstrate that low-cost sensors can capture the overall exposure with sufficient accuracy, while at the same time providing insights into variations within transport modalities. We also demonstrate that wearable sensors can capture subtle variations caused by differing routes, passenger density, location within a carriage, and other factors. For example, we demonstrate that location within the vehicle carriage can result in up to 25 % increase in daily pollution exposure – a significant difference that existing solutions are unable to capture. Finally, we highlight the practical benefits of low-cost sensors as a pollution monitoring solution by introducing applications that are enabled by low-cost wearable sensors.

Characterizing the interruption-recovery patterns of urban air pollution under the COVID-19 lockdown in China

The COVID-19 pandemic provides an opportunity to study the effects of urban lockdown policies on the variation in pollutant concentrations and to characterize the recovery patterns of urban air pollution under the interruption of COVID-19 lockdown policies. In this paper, interruption-recovery models and regression discontinuity design were developed to characterize air pollution interruption-recovery patterns and analyze environmental impacts of the COVID-19 lockdown, using air pollution data from four Chinese metropolises (i.e., Shanghai, Wuhan, Tianjin, and Guangzhou). The results revealed the air pollutant interruption-recovery curve represented by the three lockdown response periods (Level I, Level II and Level III) during COVID-19. The curve decreased during Level I (A 25.3%–48.8% drop in the concentration of NO2 has been observed in the four metropolises compared with the same period in 2018–2019.), then recovered around reopening, but decreased again during Level III. Moreover, the interruption-recovery curve of the year-on-year air pollution difference suggests a process of first decreasing during Level I and gradually recovering to a new equilibrium during Level III (e.g., the unit cumulative difference of NO2 mass concentrations in Shanghai was 21.7, 22.5, 11.3 (μg/m3) during Level I, II, and III and other metropolises shared similar results). Our findings reveal general trends in the air quality externality of different lockdown policies, hence could provide valuable insights into air pollutant interruption-recovery patterns and clear scientific guides for policymakers to estimate the effect of different lockdown policies on urban air quality.

UrbanChemFoam 1.0: large-eddy simulation of non-stationary chemical transport of traffic emissions in an idealized street canyon

Abstract. This paper describes a large-eddy simulation based chemical transport model, developed under the OpenFOAM framework, implemented to simulate dispersion and chemical transformation of nitrogen oxides from traffic sources in an idealized street canyon. The dynamics of the model, in terms of mean velocity and turbulent fluctuation, are evaluated using available stationary measurements. A transient model run using a photostationary reaction mechanism for nitrogen oxides and ozone subsequently follows, where non-stationary conditions for meteorology, background concentrations, and traffic emissions are applied over a 24 h period, using regional model data and measurements obtained for the city of Berlin in July 2014. Diurnal variations of pollutant concentrations indicate dependence on emission levels, background concentrations, and solar state. Comparison of vertical and horizontal profiles with corresponding stationary model runs at select times show that while there are only slight differences in velocity magnitude, visible changes in primary and secondary flow structures can be observed. In addition, temporal variations in diurnal profile and cumulative species concentration result in significant deviations in computed pollutant concentrations between transient and stationary model runs.

Green roofs and stormwater runoff quality in the urban landscape in South Africa

A number of sustainable urban drainage systems (SuDS), such as green roofs, are being developed and implemented in cities around the world to help reduce stormwater runoff and improve stormwater runoff quality. This study compares the water quality of green roofs with that of conventional roofs in the eThekwini region, South Africa. Samples of stormwater runoff from the different green roof systems on the eThekwini Green Roof Pilot Project were collected to test their level of contaminants and pollutants. The tests focused on all physical, aesthetic, chemical, and microbiological determinants pertaining to stormwater runoff. For all tests, the level of contaminants and pollutants were measured against the South African Water Quality Guidelines Volume 7 for Aquatic Ecosystems. The data revealed significant variations in pollutant concentrations between the green roofs and the conventional roof. Moreover, runoff water quality varied across the various roof types, which may indicate that the substrate composition has the greatest impact on green roof performance regarding rainwater quality. Overall, the results suggest that these green roof systems do not have the ability to filter pollutants out of stormwater runoff, but rather increase their levels of concentration.

The Effect of COVID-19 on the Atmospheric Parameters Over the Indian Subcontinent

In the present study, the atmospheric concentrations of various pollutants over the Indian subcontinent before the COVID-19 (during 2019) and during COVID-19 phases (2020) were studied. The COVID-19 has created a negative impact on the country’s economy but has positivity over the atmospheric resources. The levels of carbon monoxide (CO), nitrogen dioxide (NO2), and UV aerosol index were assessed using satellite images for the two different phases. The obtained results can be interpreted and can be substantiated with the lockdown effect due to the COVID -19 pandemic. The pollutants are mostly emitted from anthropogenic sources like vehicular emissions, industrial emissions, power plants, construction works, commercial and institutional places. It was evident that the levels of carbon monoxide, nitrous dioxide and aerosols levels have drastically decreased during the lockdown period. Hence, it can be concluded that COVID-19 has cleaned the atmospheric pollution as well as climate change scenario and nature on its own. © 2021 Technoscience Publications. All rights reserved.

Impact of Black Carbon on Surface Ozone in the Yangtze River Delta from 2015 to 2018

Despite the yearly decline in PM2.5 in China, surface ozone has been rapidly increasing recently, which makes it imperative to coordinate and control both PM2.5 and ozone in the atmosphere. This study utilized the data of pollutant concentrations and meteorological elements during 2015 to 2018 in Nanjing, China to analyze the daily correlation between black carbon and ozone (CBO), and the distribution of the pollutant concentrations under different levels of CBO. Besides, the diurnal variations of pollutant concentrations and meteorological elements under high positive and negative CBO were discussed and compared. The results show that the percentage of positive CBO had been increasing at the average rate of 7.1%/year, and it was 38.7% in summer on average, nearly twice of that in other seasons (19.2%). The average black carbon (BC), PM2.5 and NO2 under positive CBO was lower than those under negative CBO. It is noticeable that the surface ozone began to ascend when CBO was up to 0.2, with PM2.5 and NO2 decreasing and BC remaining steady. Under negative CBO, pollutant concentrations and meteorological elements showed obvious diurnal variations: BC showed a double-peak pattern and surface ozone, PM2.5, SO2 and CO showed single-peak patterns and NO2 showed a trough from 10:00 to 19:00. Wind speed and visibility showed a single-peak pattern with little seasonal difference. Relative humidity rose first, then it lowered and then it rose. Under positive CBO, the patterns of diurnal variations became less obvious, and some of them even showed no patterns, but just fluctuated at a certain level.

Spatio-temporal modelling of changes in air pollution exposure associated to the COVID-19 lockdown measures across Europe

The lockdown and related measures implemented by many European countries to stop the spread of the SARS-CoV-2 virus (COVID-19) pandemic have altered the economic activities and road transport in many cities. To rigorously evaluate how these measures have affected air quality in Europe, we developed Bayesian spatio-temporal (BST) models that assess changes in the surface nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentration across the continent. We fitted BST models to measurements of the two pollutants in 2020 using a lockdown indicator covariate, while accounting for the spatial and temporal correlation present in the data. Since other factors, such as weather conditions, local combustion sources and/or land surface characteristics may contribute to the variation of pollutant concentrations, we proposed two model formulations that allowed the differentiation between the variations in pollutant concentrations due to seasonality from the variations associated to the lockdown policies. The first model compares the changes in 2020, with the ones during the same period in the previous five years, by introducing an offset term, which controls for the long-term average concentrations of each pollutant during 2014–2019. The second approach models only the 2020 data, but adjusts for confounding factors. The results indicated that the latter can better capture the lockdown effect. The measures taken to tackle the virus in Europe reduced the average surface concentrations of NO2 and PM2.5 by 29.5% (95% Bayesian credible interval: 28.1%, 30.9%) and 25.9% (23.6%, 28.1%), respectively. To our knowledge, this research is the first to account for the spatio-temporal correlation present in the monitoring data during the pandemic and to assess how it affects estimation of the lockdown effect while accounting for confounding. The proposed methodology improves our understanding of the effect of COVID-19 lockdown policies on the air pollution burden across the continent.

Cross-boundary transport and source apportionment for PM2.5 in a typical industrial city in the Hebei Province, China: A modeling study

Cross-boundary transport of air pollution is a difficult issue in pollution control for the North China Plain. In this study, an industrial district (Shahe City) with a large glass manufacturing sector was investigated to clarify the relative contribution of fine particulate matter (PM2.5) to the city's high levels of pollution. The Nest Air Quality Prediction Model System (NAQPMS), paired with Weather Research and Forecasting (WRF), was adopted and applied with a spatial resolution of 5 km. During the study period, the mean mass concentrations of PM2.5, SO2, and NO2 were observed to be 132.0, 76.1, and 55.5 μg/m3, respectively. The model reproduced the variations in pollutant concentrations in Shahe at an acceptable level. The simulation of online source-tagging revealed that pollutants emitted within a 50-km radius of downtown Shahe contributed 63.4% of the city's total PM2.5 concentration. This contribution increased to 73.9±21.2% when unfavorable meteorological conditions (high relative humidity, weak wind, and low planetary boundary layer height) were present; such conditions are more frequently associated with severe pollution (PM2.5 ≥ 250 μg/m3). The contribution from Shahe was 52.3±21.6%. The source apportionment results showed that industry (47%), transportation (10%), power (17%), and residential (26%) sectors were the most important sources of PM2.5 in Shahe. The glass factories (where chimney stack heights were normally < 70 m) in Shahe contributed 32.1% of the total PM2.5 concentration in Shahe. With an increase in PM2.5 concentration, the emissions from glass factories accumulated vertically and narrowed horizontally. At times when pollution levels were severe, the horizontally influenced area mainly covered Shahe. Furthermore, sensitivity tests indicated that reducing emissions by 20%, 40%, and 60% could lead to a decrease in the mass concentration of PM2.5 of of 12.0%, 23.8%, and 35.5%, respectively.

Effectiveness of road dust suppressants: insights from particulate matter-related health damage

Although dust suppressants are widely applied to control road dust pollution, a consensus on their effectiveness has not been reached. To evaluate the effectiveness of dust suppressants (a calcium-magnesium complex) from health risks and health damage, spraying and sampling activities were conducted at four sites in Beijing. Using inhalation risk model and health damage assessment, health risks of PMx for three sensitive occupational groups were calculated and converted to life and economic loss. Results revealed that dust suppressants can indeed mitigate PM pollution and its accompanying health risks and health damage in road dust, but at a limited efficiency. By spraying dust suppressants, the total PM-related life loss reduced by 1.60E-02years and 2.50E-04years in urban and suburban areas on average, and the total willingness to pay (WTP) values decreased by 120 and 50 US$ for PM2.5 and PM10, indicating a more considerable environmental gain if dust suppressants were sprayed in additional regions when necessary. Overall, our study demonstrated that the effectiveness of dust suppressants cannot be pictured only by the variations of pollutant concentrations, and indicators with practice and economic value should be more useful for traffic-related pollution management.

An investigation into the impact of variations of ambient air pollution and meteorological factors on lung cancer mortality in Yangtze River Delta.

Lung cancer (LC) mortality, as one of the top cancer deaths in China, has been associated with increased levels of exposure to ambient air pollutants. In this study, different lag times on weekly basis were applied to study the association of air pollutants (PM2.5, PM10, and NO2) and LC mortality in Ningbo, and in subpopulations at different age groups and genders. Furthermore, seasonal variations of pollutant concentrations and meteorological variables (temperature, relative humidity, and wind speed) were analysed. A generalised additive model (GAM) using Poisson regression was employed to estimate the effect of single pollutant model on LC mortality in Yangtze River Delta using Ningbo as a case study. It was reported that there were statistically significant relationships between lung cancer mortality and air pollutants. Increases of 6.2% (95% confidence interval [CI]: 0.2% to 12.6%) and 4.3% (95% CI: 0.1% to 8.5%) weekly total LC mortality with a 3-week lag time were linked to each 10μg/m3 increase of weekly average PM2.5 and PM10 respectively. The association of air pollutants (PM2.5, PM10 and NO2) and LC mortality with a 3-week lag time was also found statistically significant during periods of low temperature (T < 18°C), low relative humidity (H < 73.7%) and low wind speed (u < 2.8m/s), respectively. The female population was found to be more susceptible to the exposure to air pollution than the male population. In addition, the population with an age of 50years or above was shown to be more sensitive to ambient air pollutant. These outcomes indicated that increased risk of lung cancer mortality was evidently linked to exposure to ambient air pollutant on a weekly basis. The impact of weekly variation on the LC mortality and air pollutant levels should be considered in air pollution-related health burden analysis.

Assessing the Distribution of Air Pollution Health Risks within Cities: A Neighborhood-Scale Analysis Leveraging High-Resolution Data Sets in the Bay Area, California.

Background:Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations.Objectives:We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California.Methods:We estimated mortality and morbidity attributable to nitrogen dioxide (), black carbon (BC), and fine particulate matter [PM in aerodynamic diameter ()] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of and BC; and b) models predicting annual , BC and concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates.Results:Estimated pollution-attributable deaths per 100,000 people at the grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for [ (95% CI: 9, 50)], BC [ (95% CI: 1, 2)], and , [ (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell–level mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring (95% CI: 19, 107) deaths per 100,000 people; (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both and , with more spatial heterogeneity at the grid-cell–level [ CBG deaths per 100,000 people (95% CI: 12, 68); (95% CI: 11, 64); (95% CI: 40, 77); and (95% CI: 37, 71)].Discussion:Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679

Appraisal of Temporal Variations in Atmospheric Compositions over South Asia by Addition of Various Pollutant’s in Recent Decade

Atmosphere is an envelope of gasses and aerosols around the planet, 99% of the total mass of atmospheric gases resides within 32km from Earth’s surface in vertical column. From primordial era to current scenario composition of earth endured numerous drastic modifications. In last decade atmosphere had undergone a vigorous change by the addition of many pollutants in both natural and anthropogenic aspects. South Asia is a densely populated; masses here are in a transition state, this developing nation in this region considerably done enough damage to the atmosphere of south Asia by inserting multiple pollutants in atmosphere in a number of anthropogenic activities. These pollutants piled up as a serious danger for people around the globe like Methane (CH4), Sulphur Dioxide (SO2), Carbon Monoxide (CO) Nitrogen Dioxide (NO2), Carbon Dioxide (CO2), Formaldehydes (HCHO) and tropospheric Ozone (O3) etc. “Environmental Remote Sensing” has arisen as a great tool of modern era to get fruitful and precise results to monitor these variations in atmospheric pollutants. The NASA’s (National Aeronautics and Space Administration), Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) system provides access to a wide variety of NASA’s remote sensing data, Variety of environmental data types has permitted the use of Giovanni for different applications to define addition and increase in concentration of various pollutants. Spatio temporal variation of pollutants shows their concentration increased in last decade and in last three years the concentration boosted as compared to last seven years.

Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR\u2013SVR\u2013ARMA combined model

In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy of the six types of pollutants in the air. First, the main information of factors affecting air quality is extracted by principal component analysis, and then principal component regression is used to give the predicted values of six types of pollutants. Second, the support vector regression machine is used to regress the predicted value of principal component regression and various influencing factors. Finally, the autoregressive moving average model is used to correct the residual items, and finally the predicted values of six types of pollutants are obtained. The experimental results showed that the proposed combination prediction model of PCR–SVR–ARMA had a better prediction effect than the artificial neural network, the standard support vector regression machine, the principal component regression, and PCR–SVR method. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and relative Mean Absolute Percent Error (MAPE) are used as evaluation indicators to evaluate the PCR–SVR–ARMA model. This model can increase the accuracy of self-built points by 72.6% to 93.2%, and the model has excellent prediction effects in the training set and detection set, indicating that the model has good generalization ability. This model can play an active role scientific arrangement and promotion of miniature air quality detectors and grid-based monitoring of the concentration of various pollutants.

New analytical approach to monitoring air quality in historical monuments through the isotopic ratio of CO2.

In this study, we evaluated indoor air quality to highlight the effects of environmental pollution in the field of cultural heritage. In particular, two important archeological places in the old part of the city of Salerno, Italy, were analyzed: Fruscione Palace and S. Pietro a Corte. The work focused on the influence of tourists on environmental pollution correlated to indoor air quality during some social and cultural events. Moreover, we focused on the possible use of the carbon isotopic composition of CO2 as a tool for environmental studies in the field of cultural heritage. The results showed a good relationship between the isotopic composition of CO2 and the variation of pollutants concentration in the air, demonstrating that it is a valid tool and non-invasive marker to monitor environmental pollution of museums and cultural heritage sites.