Lockdown during COVID-19 pandemic: A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PM_2.5 and NO₂ concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM_2.5 and NO₂ concentration in urban cities, causing low concentrations. It is observed that the original PM_2.5 and NO₂ concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM_2.5 and NO₂ concentrations. Hurst exponent of the PM_2.5 and NO₂ concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.     

Assessment of air quality during 19th Common Wealth Games at Delhi, India

The 19th Common Wealth Games was organized at Delhi, India, during October 3 to 14, 2010, where more than 8,000 athletes from 71 Commonwealth Nations have participated. In order to give them better environment information for proper preparedness, mass concentrations of particulate matters below 10 microns (PM₁₀) and 2.5 microns (PM_2.5), black carbon (BC) particles and gaseous pollutants such as carbon monoxide (CO) and nitrogen oxide (NO) were monitored and displayed online for ten different locations around Delhi, including inside and outside the stadiums. This extensive information system for air quality has been set up for the period from September 24 to October 21, 2010, and data have been archived at 5-min interval for further research. During the study period, average concentration of PM₁₀ and PM_2.5 was observed to be 229.7 ± 85.5 and 112.1 ± 56.0 μg m^?3, respectively, which is far in excess of the corresponding annual averages, stipulated by the national ambient air quality standards. Significant large and positive correlation (r = 0.93) between PM₁₀ and PM_2.5 implies that variations in PM₁₀ mass are governed by the variations in PM_2.5 mass. The mass concentrations of PM_2.5 inside the stadium were found to be ~18 % lower than those outside; however, no large variations were observed in PM₁₀. Mean concentrations of BC, CO and NO for the observation period were 10.9 μg m^?3 (Min, 02 μg m^?3; Max, 31 μg m^?3), 1.83 ± 0.89 ppm (Min, 0.48 ppm; Max, 4.55 ppm) and 37.82 ppb (Min, 2.4 ppb; Max, 206.05 ppb), respectively. BC showed positive correlation (r = 0.73) with CO suggests unified source for both of them, mainly from combustion emissions. All the measured parameters, however, show a significant diurnal variation with enhanced peaks in the morning and late night hours and lower values during daytime.     

Statistical evaluation of PM<Subscript>10</Subscript> and distribution of PM<Subscript>1</Subscript>, PM<Subscript>2.5</Subscript>, and PM<Subscript>10</Subscript> in ambient air due to extreme fireworks episodes (Deepawali festivals) in megacity Delhi

Temporal variation of PM₁₀ using 2-year data (January, 2007–December, 2008) of Delhi is presented. PM₁₀ varied from 42 to 200 μg m⁻³ over January to December, with an average 114.1 ± 81.1 μg m⁻³. They are comparable with the data collected by Central Pollution Control Board (National Agency which monitors data over the entire country in India) and are lower than National Ambient Air Quality (NAAQ) standard during monsoon, close to NAAQ during summer but higher in winter. Among CO, NO₂, SO₂, rainfall, temperature, and wind speed, PM₁₀ shows good correlation with CO. Also, PM₁₀, PM_2.5, and PM₁ levels on Deepawali days when fireworks were displayed are presented. In these festive days, PM₁₀, PM_2.5, and PM₁ levels were 723, 588, and 536 μg m⁻³ in 2007 and 501, 389, and 346 μg m⁻³ in 2008. PM₁₀, PM_2.5, and PM₁ levels in 2008 were 1.5 times lower than those in 2007 probably due to higher mixing height (446 m), temperature (23.8°C), and winds (0.36 ms⁻¹).     

Tropospheric NO2 variability over the metropolitan cities and hill stations of India

Monthly and inter-annual variation in tropospheric nitrogen dioxide (NO₂) have been examined over metropolitan cities (New Delhi, Kolkata, Mumbai and Chennai) and hill stations (Mount Abu, Nainital, Srinagar, Kodaikanal, Dalhousie, Gulmarg, Shimla and Munnar) of India during the period 2004?C2010 using satellite-based SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). It is observed that the monthly variation in NO₂ over the metropolitan cities is higher during winter (November?CDecember?CJanuary?CFebruary) months and lower during summer monsoon (June?CJuly?CAugust?CSeptember) months. Lower NO₂ in summer monsoon leads to the presence of deep convection and higher in winter leads to calm winds and more residential time of gases. Moreover, rapid industrialization and traffic growth are also responsible for the higher NO₂. Mean values of NO₂ over New Delhi and Mumbai as well as hill stations, such as Mount Abu, Nainital and Shimla, have exhibited more pollution. Similarly, maximum NO₂ occurred over the hill stations during pre-monsoon months (April?CMay) and early part of summer monsoon (June). Higher NO₂ values are observed in November?CDecember months. All the hill stations also show increasing trend of NO₂ during the period 2004?C2010. Increasing pollution of NO₂ over the hill stations might also be due to forest fires, biomass burning and long-range transport. Back trajectory analysis shows that the observed peaks in NO₂ are a resultant of the long-range transported component amplified by the local environment. In the northern hill stations, pollution seems transported from west Asian and European countries while in the southern hill stations, pollution is originated from southern Indian Ocean and East Asian countries.     

The characteristics of air pollutants during different seasons in the urban area of Lanzhou,Northwest China

Based on data from ground-based air quality stations, space–time variations of six principal atmospheric pollutants, such as particulate matter (PM_2.5 and PM₁₀) and gas pollutants (SO₂, NO₂, СО, and O₃), obtained from January 1, 2014 to December 31, 2017 in the city of Lanzhou, have been studied. Average total concentrations of PM_2.5 and PM₁₀ were 53.2?±?26.91 and 124.54?±?82.33 µg/m³, respectively; however, the results showed that in 75.53% and 84.85% days, concentrations of these pollutants exceeded Chinese National Ambient Air Quality Standard and in 100% days exceeded World Health Organization guidelines standards. Daily mean values of aerosol optical depth and Ångström exponent based on data, received by satellite Moderate Resolution Imaging Spectroradiometer, show a broad range of values for aerosol optical depth (from 0.018 to 1.954) and Ångström exponent (from 0.003 to 1.8). Results of principal components analysis revealed three factor loadings. Thus, Factor 1 has the relevant loadings for PM_2.5, PM₁₀, CO, SO₂, and NO₂ (36%) and closely associated with transport emissions and industrial sources, which contribute to air pollution in Lanzhou. Factor 2 was heavily loaded with temperature and visibility (16.94%). Factor 3 consisted of relative humidity (14.11%). Cluster analysis revealed four subgroups: cluster 1 (PM_2.5, NO₂, SO₂), cluster 2 (CO), cluster 3 (PM₁₀) and cluster 4 (relative humidity, visibility, temperature, O₃, wind speed), which were compliant with results, obtained from principal components analysis. Positive correlation was found among all pollutants, other than O₃. According to processed backward trajectories obtained by Hybrid Single-Particle Lagrangian Integrated Trajectory model, it was found that movement of air masses occur from north, northwest, and west directions—the location of principal natural sources of aerosols.     

Long-term variations in outgoing long-wave radiation (OLR), convective available potential energy (CAPE) and temperature in the tropopause region over India

Relationship of outgoing long-wave radiation (OLR) with convective available potential energy (CAPE) and temperature at the 100-hPa pressure level is examined using daily radiosonde data for a period 1980–2006 over Delhi (28.3°N, 77.1°E) and Kolkata (22.3°N, 88.2°E), and during 1989–2005 over Cochin (10°N, 77°E) and Trivandrum (8.5°N, 77.0°E), India. Correlation coefficient (R _xy) between monthly OLR and CAPE shows a significant (~???0.45) anti-correlation at Delhi and Kolkata suggesting low OLR associated with high convective activity during summer (seasonal variation). Though, no significant correlation was found between OLR and CAPE at Cochin and Trivandrum (low latitude region); analysis of OLR and temperature (at 100-hPa) association suggests that low OLR peaks appear corresponding to low temperature at Delhi (R _xy~ 0.30) and Kolkata (R _xy ~ 0.25) during summer. However, R _xy between OLR and temperature becomes opposite as we move towards low latitudes (~8°–10°N) due to strong solar cycle influence. Large scale components mainly ENSO and quasi-biennial oscillaton (QBO) that contributed to the 100-hPa temperature variability were also analyzed, which showed that ENSO variance is larger by a factor of two in comparison to QBO over Indian region. ENSO warm conditions cause warming at 100-hPa over Delhi and Darwin. However, due to strong QBO and solar signals in the equatorial region, ENSO signal seems less effective. QBO, ENSO, and solar cycle contribution in temperature are found location-dependent (latitudinal variability) responding in consonance with shifting in convective activity regime during El Niño, seasonal variability in the tropical easterly jet, and the solar irradiance.     

Assessment of spatial variations of particulate matter (PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript>) in Bahrain identified by air quality index (AQI)

The rapid urbanization, industrialization, modernization, and the frequent Middle Eastern dust storms have negatively impacted the ambient air quality in Bahrain. The objective of this study is to identify the most critical atmospheric air pollutants with emphasis on their potential risk to health based on calculated AQI (air quality index) values using EPA approach. The air quality datasets of particulate matters (PM₁₀ and PM_2.5), ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) were measured in January 2012 and August 2012 using five mobile air quality monitoring stations located at different governorates. The results of this study demonstrated that PM₁₀ and PM_2.5 are the most critical air pollutants in Bahrain with PM_2.5 prevailing during January 2012 and PM₁₀ prevailing during August 2012. The corresponding AQI categories were utilized to evaluate spatial variability of particulate matters in five governorates. The impact of meteorological factors such as ambient air temperature, wind speed, relative humidity, and total precipitation on ambient air quality were discussed. The analysis demonstrated that the highest PM₁₀ concentrations were observed in the Northern Governorate while the highest PM_2.5 concentrations were observed in the Capital, Central, and Northern Governorates during August 2012. It was observed that the levels of PM_2.5 pollution were higher within proximity of the industrial zone. The results suggested that the average PM_2.5/PM₁₀ ratio in August 2012 was lower than in January 2012 due to the Aeolian processes. This study concludes that higher wind speed, total precipitation, relative humidity rates, and lower ambient air temperature in January 2012 assisted with the dissipation of particulate matter thus lowering the pollution levels of both PM₁₀ and PM_2.5 in comparison to August 2012.     

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM_2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from –3.4 µg/m³ to –1.6 µg/m³. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM_2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM_2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM_2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.     

Temporal analysis of air pollution and its relationship with meteorological parameters in Bahrain, 2006–2012

The objective of this paper is to analyze temporal and seasonal trends of air pollution in Bahrain between 2006 and 2012 by utilizing datasets from five air quality monitoring stations. The non-parametric and robust Theil-Sen approach is employed to study quantitatively temporal variations of particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃). The calculated annual concentrations for PM₁₀ and PM_2.5 in Bahrain were substantially higher than recommended World Health Organization (WHO) guideline standards. Results showed increasing trends for PM₁₀, PM_2.5, and SO₂ whereas O₃ and its precursor NO₂ showed decreasing behavior. The general increase in air pollution trends is in agreement with prediction of air pollution models for Middle East region due to economic growth, industrialization, and urbanization. The significances of long-term trends were examined. Additional to actual (unadjusted) trends, meteorological adjusted (deseasonalized) trends and seasonal trends were quantified. The box-plot analysis visually illustrated monthly variations of key air pollutants. It showed that only PM₁₀ and PM_2.5 exhibited seasonal pattern, and their concentrations increased during summer and decreased during winter. The effects of ambient air temperature, relative humidity, wind speed, and rainfall on particulate matter (PM) concentrations were further investigated. The Spearman correlation coefficient results demonstrated significant negative correlation between relative humidity and PM concentrations (??0.595 for PM₁₀ and ??0.526 for PM_2.5) while significant positive correlation was observed between temperature and PM concentrations (0.420 for PM₁₀ and 0.482 for PM_2.5).     

Assessment of carbonaceous aerosol over Delhi in the Indo-Gangetic Basin: characterization, sources and temporal variability

Semi-continuous measurements of organic carbon (OC) and elemental carbon (EC) and continuous measurements of black carbon (BC) and PM_2.5 aerosols were conducted simultaneously during the winter period of 2010–2011 at Delhi, one of the polluted urban megacities in western part of the Indo-Gangetic Basin region. The average mass concentrations of OC, EC, BC and PM_2.5 were about 54 ± 39, 10 ± 5, 12 ± 5 and 210 ± 146 μg m^?3, respectively. Contribution of total carbonaceous aerosol mass to PM_2.5 mass was found to be ~46 %. Average OC/EC ratio was found to be 5 ± 2 during the study period, suggesting the presence of secondary organic aerosols in the atmosphere over Delhi. Estimated mean secondary organic aerosol mass concentration was found to be 25 μg m^?3 and varied between 14.6 (February) and 37.0 μg m^?3 (December). A diurnal variation of OC and EC shows lower values during the day time and higher during the morning and night, which are highly associated with the corresponding variability in mixing layer heights. OC and EC were also found to be significantly correlated (r = 0.71) to each other, indicating their common sources. Concentrations of OC and EC were about 45 and 13 % higher during weekdays than weekends, respectively. Higher OC (67 %) and EC (53 %) were observed in the late evening during weekdays than those on weekends, which could be due to different emission sources during these two periods. The night/day ratio of EC and OC was found to be larger than 1.0, suggesting the relative accumulation of EC and OC near the surface at night hours.     

Spatio-temporal variations of respirable particles at residential areas located in the vicinity of opencast coal projects,India: a case study

The objective of the study is to investigate spatio-temporal variations of PM₁₀, PM_2.5, and PM₁ concentrations at seven residential sites, located in the vicinity of opencast coal projects, Basundhara Garjanbahal Area (BGA), India. Meteorological parameters such as wind speed, wind direction, relative humidity, and temperature were collected simultaneously with PM concentrations. Mean concentrations of PM₁₀ in the range 215 ± 169–526 ± 412 μg m^?3, PM_2.5 in the range of 91 ± 79–297 ± 107 μg m^?3, PM₁ in the range of 68 ± 60–247 ± 84 μg m^?3 were obtained. Coarse fractions (PM_2.5–10) varied from 27 to 58% whereas fine fractions (PM_1–2.5 and PM₁) varied in the range of 51–73%. PM_2.5 concentration was 41–74% of PM₁₀ concentration, PM₁ concentration was 31–62% of PM₁₀ concentration, and PM₁ concentration was 73–83% of PM_2.5 concentration. Role of meteorology on PM concentrations was assessed using correlation analysis. Linear relationships were established among PM concentrations using least square regression analysis. With the aid of principal component analysis, two components were drawn out of eight variables, which represent more than 75% of variance. The results indicated that major sources of air pollutants (PM₁₀, PM_2.5, PM₁, CO, CO₂) at the residential sites are road dust raised by vehicular movement, spillage of coal generated during transportation, spontaneous combustion of coal, and biomass burning in village area.     

Spatial variation of probabilistic seismic hazard for Mumbai and surrounding region

Mumbai city, the economical capital of India, is located on the west coast of stable intra-plate continental region of Peninsular India which has an experience of significant historical earthquakes in the past. The city stood as the fourth most populous city in the world. Recent seismo-tectonic studies of this city highlighted the presence of active West coast fault and Chiplun fault beneath the Deccan basalt. In the present study, spatial variability of probabilistic seismic hazard for Mumbai region (latitudes of 18.85–19.35°N and longitudes of 72.80–73.15°E at a grid spacing of 0.05°) which includes Mumbai city, Suburban, part of Thane district and Navi Mumbai, in terms of ground motion parameters; peak horizontal acceleration and spectral acceleration at 1.0-s period for 2 and 10 % probability of exceedance in 50 years are generated. The epistemic uncertainty in hazard estimation is accounted by employing seven different ground motion prediction equations developed for worldwide shallow crustal intra-plate environments. Further, the seismic hazard results are deaggregated for Mumbai (latitude 18.94°N, longitude 72.84°E) to understand the relative contributions of earthquake sources in terms of magnitude and distance. The generated hazard maps are compared with the zoning specified by Indian seismic code (IS1893: Part 1 in Indian standard criteria for earthquake-resistant design of structures, Part 1—General provisions and buildings. Bureau of Indian Standards, New Delhi, India, 2002) for rocky site. Present results show an underestimation of potential seismic hazard in the entire study region by non-probabilistic zoning prescribed by IS1893: Part 1 with significantly higher seismic hazard values in the southern part of Navi Mumbai.     

Probing for overshooting as extreme event of thunderstorms

Thunderstorm overshooting is rare but not an unusual phenomenon in a metropolitan of India, Kolkata (22.57° N; 88.36° E) during the pre-monsoon months (April–May). An attempt is made in this study to identify the important parameters differentiating the thunderstorms in overshooting and non-overshooting categories through data analytics from 2000 to 2015. The present investigation on parametric classification would facilitate in estimating the predictability of thunderstorms with overshooting which subsequently might assist in operational forecast of thunderstorm severity over Kolkata. The altitudes of lifting condensation level (LCL), wind shear, bulk Richardson number (BRN), gust speed, boundary layer characteristics and their correlation with thunderstorm cloud top height (CTH) and also their variation and distribution during overshooting (OTS) and non-overshooting (TS) thunderstorms are analyzed in this study. The result depicts that over Kolkata the intensity of storms during OTS is higher than during TS though the frequency of OTS is less than that of TS. The results further show that the potential temperature (θ), equivalent potential temperature (θ_e), mixing ratio (e_s) in the boundary layer, convective available potential energy, convective inhibition energy, BRN and gust speed play significant roles in regulating the CTH during OTS and TS thunderstorms over Kolkata.

     

Spatial heterogeneity in near surface aerosol characteristics across the Brahmaputra valley

In order to examine the spatial variability of the aerosol characteristics across the Brahmaputra valley, a land campaign was conducted during late winter (February 3–March 2) 2011. Measurements of particulate matter (PM, PM₁₀, PM_2.5) and black carbon (BC) concentrations were made onboard an interior redesigned vehicle. The length of the campaign trail stretched about 700 km, covering the longitude belt of 89.97°–95.55°E and latitude belt of 26.1°–27.6°N, comprising 13 measurement locations. The valley is divided into three sectors longitudinally: western sector (R1: 89.97°–91.75°E), middle sector (R2: 92.5°–94.01°E) and eastern sector (R3: 94.63°–95.55°E). Spatial heterogeneity in aerosol distribution has been observed with higher PM₁₀ and PM_2.5 concentrations at the western and middle sectors compared to the eastern sector. The locations in the western sector are found to be rich in BC compared to the other two sectors and there is a gradual decrease in BC concentrations from west to east of the Brahmaputra valley. Two hotspots within the western and middle sectors with high PM and BC concentrations have been identified. The associated physico-optical parameters of PM reveal abundance of PM_2.5 aerosols along the entire valley. High population density in the western and middle sectors, together with the contribution of remote aerosols, leads to higher anthropogenic aerosols over those regions. Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) slightly underestimates the measured PM₁₀ and PM_2.5 at the eastern sector while the model overestimates the measurements at a number of locations in the western sector. In general, BC is underestimated by the model. The variation of BC within the campaign trail has not been adequately captured by the model leading to higher variance in the western locations as compared to the middle and eastern locations.     

High resolution aerosol data from MODIS satellite for urban air quality studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM_2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R² =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM_2.5 ground concentrations. Finally, we studied the relationship between PM_2.5 and AOD at the intra-urban scale (?10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM_2.5 relationship does not depend on relative humidity and air temperatures below ～7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM_2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM_2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM_2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R² of 0.86). Therefore, adjustment for the daily variability in the AOD-PM_2.5 relationship provides a means for obtaining spatially-resolved PM_2.5 concentrations.     

Coarse and fine particulates, Hg(p)-bound particulate concentrations and compositions study at Sha-Lu, central Taiwan

Ambient air and coarse, fine and particulate-bound mercury (Hg(p)) pollutants were collected and analyzed from March 17 to May 22 and September 3, 2009 to March 5, 2010 at a highway traffic site located in Sha-Lu, central Taiwan. This study has the following objectives: (1) to measure the coarse and fine particulates concentrations and the particulate-bound mercury Hg(p) which was attached to these particulate; (2) to determine the average Hg(p) compositions in coarse and fine particulates and (3) to compare the Hg(p) concentrations and compositions particulate in this study to the those obtained in other studies. The results obtained in this study indicated that the average ambient air PM_2.5, PM_2.5–10 and PM₁₀ were 18.79 ± 6.71, 11.22 ± 4.93 and 30.01 ± 10.27 μg/m³, respectively. The ranges of concentrations for Hg(p) in PM_2.5 were from 0.0016 to 0.0557 ng/m³, from 0.0006 to 0.0364 ng/m³ in PM_2.5–10 and from 0.0022 to 0.0862 ng/m³ in PM₁₀. In addition, the highest particle-bound mercury compositions in PM_2.5 were 16.85 ng/g and the lowest particle-bound mercury concentrations were 0.55 ng/g. The highest particle-bound mercury compositions in PM_2.5–10 were 13.88 ng/g and the lowest particle-bound mercury in PM_2.5–10 were 0.22 ng/g.     

Experimental and numerical analyses of particulate matter concentrations in underground subway station

The purpose of this paper was to perform the experimental and numerical analyses of PM₁₀ and PM_2.5 concentrations in Imam Khomeini (IKH) underground subway station in Tehran. The aim was to provide fundamental data in order to fulfill workers and passengers respiratory health necessities. Experimental measurements was done at three different locations (entrance, middle and exit) inside the platform and also outdoor ambient of the station. The Dust-Trak was applied to measure continuous PM_2.5 and PM₁₀ concentrations at a logging interval of 30 s. The measurements were recorded during rush hours (8:00 am–12:00 pm) for one week per each season from June 2015–June 2016.Moreover, computational fluid dynamic (CFD) simulation was done for the platform of the above station and the necessary boundary conditions were provided through field measurements. Those basic parameters which were considered for numerical analysis of particulate matters concentrations included air velocity, air pressure and turbulence. Furthermore, the piston effect caused by train movement inside the station provided natural ventilation in the platform. The results showed that seasonal measured PM_2.5 and PM₁₀ indoor concentrations had a variety range from 40–98 µg/m³ to 33–102 µg/m³, respectively, and were much higher than national indoor air quality standard levels. Meanwhile, PM_2.5 and PM₁₀ concentrations in the IKH underground subway station were approximately 2.5–2.9 times higher than those in outdoor ambient, respectively. Numerical simulation indicated that the predicted concentrations were underestimated by a factor of 8% in comparison with the measured ones.     

Mass-size distribution of PM10 and its characterization of ionic species in fine (PM2.5) and coarse (PM10−2.5) mode, New Delhi, India

Size distribution of PM₁₀ mass aerosols and its ionic characteristics were studied for 2 years from January 2006 to December 2007 at central Delhi by employing an 8-stage Andersen Cascade Impactor sampler. The mass of fine (PM_2.5) and coarse (PM_10?2.5) mode particles were integrated from particle mass determined in different stages. Average concentrations of mass PM₁₀ and PM_2.5 were observed to be 306 ± 182 and 136 ± 84 μg m^?3, respectively, which are far in excess of annual averages stipulated by the Indian National Ambient Air Quality Standards (PM₁₀: 60 μg m^?3 and PM_2.5: 40 μg m^?3). The highest concentrations of PM_10?2.5 (coarse) and PM_2.5 (fine) were observed 505 ± 44 and 368 ± 61 μg m^?3, respectively, during summer (June 2006) period, whereas the lower concentrations of PM_10?2.5 (35 ± 9 μg m^?3) and PM_2.5 (29 ± 13 μg m^?3) were observed during monsoon (September 2007). In summer, because of frequent dust storms, coarse particles are more dominant than fine particles during study period. However, during winter, the PM_2.5 contribution became more pronounced as compared to summer probably due to enhanced emissions from anthropogenic activities, burning of biofuels/biomass and other human activities. A high ratio (0.58) of PM_2.5/PM₁₀ was observed during winter and low (0.24) during monsoon. A strong correlation between PM₁₀ and PM_2.5 (r ² = 0.93) was observed, indicating that variation in PM₁₀ mass is governed by the variation in PM_2.5. Major cations (NH₄ ⁺, Na⁺, K⁺, Ca²⁺ and Mg²⁺) and anions (F^?, Cl^?, SO₄ ^2? and NO₃ ^?) were analyzed along with pH. Average concentrations of SO₄ ^2? and NO₃ ^? were observed to be 12.93 ± 0.98 and 10.33 ± 1.10 μg m^?3, respectively. Significant correlation between SO₄ ^2? and NO₃ ^? in PM_1.0 was observed indicating the major sources of secondary aerosol which may be from thermal power plants located in the southeast and incomplete combustion by vehicular exhaust. A good correlation among secondary species (NH⁺, NO₃ ^? and SO₄ ^2?) suggests that most of NH₄ ⁺ is in the form of ammonium sulfate and ammonium nitrate in the atmosphere. During winter, the concentration of Ca²⁺ was also higher; it may be due to entrainment of roadside dust particles, traffic activities and low temperature. The molar ratio (1.39) between Cl^? and Na⁺ was observed to be close to that of seawater (1.16). The presence of higher Cl^? during winter is due to western disturbances and probably local emission of Cl^? due to fabric bleaching activity in a number of export garment factories in the proximity of the sampling site.     

Seismic hazard in mega city Kolkata, India

The damages caused by recent earthquakes in India have been a wake up call for people to take proper mitigation measures, especially the major cities that lie in the high seismic hazard zones. Kolkata City, with thick sediment deposit (∼12 km), one of the earliest cities of India, is an area of great concern as it lies over the Bengal Basin and lies at the boundary of the seismic zones III and IV of the zonation map of India. Kolkata has been affected by the 1897 Shillong earthquake, the 1906 Calcutta earthquake, and the 1964 Calcutta earthquake. An analysis on the maximum magnitude and b-value for Kolkata City region is carried out after the preparation of earthquake catalog from various sources. Based on the tectonic set-up and seismicity of the region, five seismic zones are delineated, which can pose a threat to Kolkata in the event of an earthquake. They are broadly classified as Zone 1: Arakan-Yoma Zone (AYZ), Zone 2: Himalayan Zone (HZ), Zone 3: Shillong Plateau Zone (SPZ), Zone 4: Bay of Bengal Zone (BBZ), and Zone 5: Shield Zone (SZ). The maximum magnitude (m _max) for Zones 1, 2, 3, 4, and 5 are 8.30 ± 0.51, 9.09 ± 0.58, 9.20 ± 0.51, 6.62 ± 0.43 and 6.61 ± 0.43, respectively. A probability of 10% exceedance value in 50 years is used for each zone. The probabilities of occurrences of earthquakes of different magnitudes for return periods of 50 and 100 years are computed for the five seismic zones. The Peak Ground Acceleration (PGA) obtained for Kolkata City varies from 0.34 to 0.10 g.     

Seismic reliability-based analysis and GIS mapping of cyclic mobility of clayey soils of Mumbai city,India

Cyclic mobility is a mechanism of ground failure due to lateral spreading of soils during an earthquake that usually occurs in soft or medium stiff saturated soils. The simplified procedures developed by the researchers give a factor of safety for judging the cyclic mobility potential. However, the simplified procedures do not take into account the uncertainty in the parameters required to estimate the cyclic stresses in the soil. In this study, a reliability framework based on the simplified procedure, considering the parameter uncertainty, has been proposed for computing the probability of cyclic mobility of clay deposits for a metro city of India, i.e., Mumbai city (latitudes 18°53′N–19°19′N and longitudes 72°47′E–72°58′E). Extensive geotechnical borehole data from 1028 boreholes across 50 locations in the city area of 390 km² and laboratory test data are collected and analyzed thoroughly. A correlation between undrained shear strength (Su) and other parameters such as natural water content (w), SPT N value, liquid limit (LL) and plasticity index (PI) has been established for Mumbai city and has been used in the proposed approach. The sensitivity analysis of the proposed approach predicts that Su has significant influence in the evaluation of the cyclic mobility. Cyclic mobility hazard maps are prepared using the geo-statistical analysis tool in GIS, and it shows that the clayey soils at few locations have a 60–90 % probability of cyclic mobility for a moment magnitude (M _w) of an earthquake of 7.5. These hazard maps can be used by the geotechnical engineers for the cyclic mobility hazard assessment of Mumbai city.