The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM
10
and PM
2.5
) over Delhi. For ...analysis, the hourly concentration dataset of PM
2.5
and PM
10
from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM
10
, PM
2.5
, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM
10
was observed to be 426.77 µg/m
3
while that of PM
2.5
was observed to be 301.91 µg/m
3
in January 2019 for traffic-affected regions. During winters, higher PM
2.5
concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM
10
concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM
10
pollution over Delhi.
Investigating the relationship between thermal inertia (TI) and aerosol optical depth (AOD) is significant in giving insights into the seasonality of dust deposition and lifting phenomenon. The ...present study focuses on establishing a relationship of AOD with TI and different particle sizes over different Martian seasons. Two different Martian landforms (exposed rock and sand dunes) have been used to establish these relationships. TI layer was generated using THEMIS nighttime images for different seasons, whereas Curiosity Rover measured AOD values and Mars Climate database (MCD) visible column dust optical depth were used to derive rover equivalent AOD. An inverse relation was observed between AOD and TI for exposed rock and sand dune regions for all the seasons with low to moderate coefficient of determination (R2). A similar inverse trend was observed between rover equivalent AOD and particle size with R2 values ranging from 0.8 in the case of sand dunes (winter) to 0.93 in exposed rock (autumn). The results were further compared within the AOD obtained from orbiter image (HRSC) derived using Shadow method for spring season (Shaheen et al., 2022). The same inverse relation was found within TI having good R2 values of 0.61 for exposed rock and 0.76 for the sand dunes. Error estimation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), Fractional Bias (FB), Index of agreement errors was carried out for TI vs. AOD and particle size vs. AOD. Excellent statistical significance was obtained for AOD and particle size, in the case of sand dunes it was 0.96 for autumn and 0.99 in case of exposed rock for spring season, respectively.
•An inverse trend was observed between TI and Rover equivalent AOD over Gale crater for all the seasons.•A similar inverse trend was observed between particle size and Rover equivalent AOD for all the seasons.•Inverse trend matched well with dust lifting and deposition phenomenon over Gale crater.
The first detailed seasonal validation has been carried out for the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites Level 2.0 Collection Version 5.1 AOT (τMODIS) with ...Aerosol Robotic Network (AERONET) Level 2.0 AOT (τAERONET) for the years 2009–2012 over semi-arid region Jaipur, northwestern India. The correlation between τMODIS versus τAERONET at 550 nm is determined with different spatial and temporal size windows. The τMODIS overestimates τAERONET within a range of +0.06 ± 0.24 during the pre-monsoon (April–June) season, while it underestimates the τAERONET with −0.04 ± 0.12 and −0.05 ± 0.18 during dry (December–March) and post-monsoon (October–November) seasons, respectively. Correlation without (with) error envelope has been found for pre-monsoon at 0.71 (0.89), post-monsoon at 0.76 (0.94), and dry season at 0.78 (0.95). τMODIS is compared to τAERONET at three more ground AERONET stations in India, i.e., Kanpur, Gual Pahari, and Pune. Furthermore, the performance of MODIS Deep Blue and Aqua AOT550 nm (τDB550 nm and τAqua550 nm) with τAERONET is also evaluated for all considered sites over India along with a U.S. desert site at White Sand, Tularosa Basin, NM. The statistical results reveal that τAqua550 nm performs better over Kanpur and Pune, whereas τDB550 nm performs better over Jaipur, Gual Pahari, and White Sand High Energy Laser Systems Test Facility (HELSTF) (U.S. site).
This study seeks to understand and quantify the changes in tropospheric ozone (O
3
) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic ...Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO
2
observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O
3
concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O
3
concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m
2
in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it.
•Normalized Burn Ratio Thermal (NBRT) is used to estimate the high vegetation burning ratios over Uttarakhand.•Maximum fires are observed during April to June months over the forest area due to high ...temperature.•The results demonstrate the potential of Earth satellite data in emissions characterization and in AQ management.
Confirmed rise in average surface temperature and consequent prolonged dry days in tropical Himalayan foothills (tarai region) favors frequent wildfire event which make susceptible to the local forest vegetation and ecology. Recent improvement in spatio-temporal resolution of space-borne sensors provides an opportunity to routinely map these wildfires and estimate the consequence. Utilizing both active and passive space-borne multi-sensors, this study presents the active fire counts, columnar and vertical distribution of aerosol during 2013–2018 over Uttarakhand region of India. Our analysis shows maxima in active fire counts during April to late June months while minima in monsoon over the region. Particularly owing to the high temperature, low moisture, drying up of natural spring and availability of fuel materials in summer and scare precipitation in winter. Some limited spatio-temporal scale fire episodes are also marked in winter. The AOD values with maximum of 3.2 (0.5 mean) observed during the April 2016, while for the successive next two months, AOD of 2.0 and 1.2 are found over the fire burning regions. The Normalized Burn Ratio Thermal (NBRT) index are also found to be much higher for April and May 2016 with respect to 2015 and 2017. The comparative analysis of NBRT shows a positive difference towards the western side of Uttarakhand. Vertical feature mask and aerosol subtype profile details about the polluted dust and elevated smoke aerosols from surface to 10 km range during the intense fire events smoke were elevated and trapped within 3 to 10 km. The results demonstrate the potential of earth’s observing satellites for characterization of emissions and in air quality management.
Lightning flashes (LF) and their association with meteorological variables that can influence the occurrence of lightning have been assessed in detail over the Indian domain, i.e., the Convective ...Available Potential Energy (CAPE), Relative Humidity (RH), Total Column Water Vapour (TCWV), Surface Temperature (ST) and Outgoing Longwave Radiation (OLR). A high-resolution dataset of LF has been retrieved from the Lightning Imaging Sensor (LIS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. The CAPE, TCWV, RH and ST from 1998 to 2013 are retrieved using ERA-Interim monthly/annual climatology while OLR was retrieved from NCEP datasets. The seasonal analysis shows that most LF occur during the pre-monsoon period (March, April, and May) (0.40–0.45 flash/km
2
/day) over northeast region. During the monsoon season (June, July, and August), the LF dominates over northern India (0.35–0.40 flash/km
2
/day). The seasonal variation of CAPE shows the maximum (1250–2250 J/kg) during pre-monsoon over the coastal area of NE and SE regions. TCWV and RH show the maximum in monsoon season over northeastern part, which is 50–70 kg/m
2
and 60–80%, respectively. The dependence of LF on meteorological parameters varies from region to region, as is evident from statistical analysis. Maximum LF occurred over the NE (0.049 flash/km
2
/day) region, followed by the EC (0.041 flash/km
2
/day) and the lowest in the WC (0.027 flash/km
2
/day) region of India. The LF showed a significant correlation with CAPE over NE and EC of India because of higher humidity content values over the coastal regions, which form graupel through convection. Over the WH, LF and CAPE showed a good correlation (
r
= 0.94) because of orographic convection processes. Further, TCWV showed significant correlation with LF over WH (0.89) and minimum over WC (0.23) region. Principal component analysis (PCA) shows that lightning is well correlated with CAPE, RH, TCWV and ST over most regions in India. However, lightning is not significantly correlated with OLR. Understanding the meteorology of lightning across the Indian region can inform forecasting of possible lightning events and is relevant for assessing lightning for human, wild risk and climate projections.
Research highlights
The regional-scale meteorological variables associated with lightning are identified.
Due to the regional orography, lightning flashes show high correlation in proximity to meteorological variables.
The spatio-temporal distribution shows that most of the lightning flashes occur during March, April and May (MAM) months over North East region.
The impact of meteorological variables is visible as the study's threshold values change over time.
The collocated measurements of aerosols size distribution (ASD) and aerosol optical thickness (AOT) are analyzed simultaneously using Grimm aerosol spectrometer and MICROTOP II Sunphotometer over ...Jaipur, capital of Rajasthan in India. The contrast temperature characteristics during winter and summer seasons of year 2011 are investigated in the present study. The total aerosol number concentration (TANC, 0.3–20μm) during winter season was observed higher than in summer time and it was dominated by fine aerosol number concentration (FANC<2μm). Particles smaller than 0.8μm (at aerodynamic size) constitute ~99% of all particles in winter and ~90% of particles in summer season. However, particles greater than 2μm contribute ~3% and ~0.2% in summer and winter seasons respectively. The aerosols optical thickness shows nearly similar AOT values during summer and winter but corresponding low Angstrom Exponent (AE) values during summer than winter, respectively. In this work, Potential Source Contribution Function (PSCF) analysis is applied to identify locations of sources that influenced concentrations of aerosols over study area in two different seasons. PSCF analysis shows that the dust particles from Thar Desert contribute significantly to the coarse aerosol number concentration (CANC). Higher values of the PSCF in north from Jaipur showed the industrial areas in northern India to be the likely sources of fine particles. The variation in size distribution of aerosols during two seasons is clearly reflected in the log normal size distribution curves. The log normal size distribution curves reveals that the particle size less than 0.8μm is the key contributor in winter for higher ANC.
•The number size distribution was dominated by fine particles during winter season.•The log normal size distribution curves reveals that the particle size <0.8µm is key contributor in winter for higher ANC.•The average AE values differs considerably during summer (<0.3) than winter (>1.0) season.•Interestingly, mean AOT values during summer and winter months do not differ largely over the study site.•Potential Source Contribution Function (PSCF) suggests coarse particles at site came from south-west direction (Thar Desert).
In this study, we systematically document the link between dust episodes and local scale regional aerosol optical properties over Jaipur located in the vicinity of Thar Desert in the northwestern ...state of Rajasthan. The seasonal variation of AOT
500 nm
(aerosol optical thickness) shows high values (0.51 ± 0.18) during pre-monsoon (dust dominant) season while low values (0.36 ± 0.14) are exhibited during winter. The Ångström wavelength exponent has been found to exhibit low value (<0.25) indicating relative dominance of coarse-mode particles during pre-monsoon season. The AOT increased from 0.36 (April
mean
) to 0.575 (May–June
mean
). Consequently, volume concentration range increases from April through May–June followed by a sharp decline in July during the first active phase of the monsoon. Significantly high dust storms were observed over Jaipur as indicated by high values of single scattering albedo (SSA
440 nm
= 0.89, SSA
675 nm
= 0.95, SSA
870 nm
= 0.97, SSA
1,020 nm
= 0.976) than the previously reported values over IGP region sites. The larger SSA values (more scattering aerosol), especially at longer wavelengths, is due to the abundant dust loading, and is attributed to the measurement site’s proximity to the Thar Desert. The mean and standard deviation in SSA and asymmetry parameter during pre-monsoon season over Jaipur is 0.938 ± 0.023 and 0.712 ± 0.017 at 675 nm wavelength, respectively. Back-trajectory air mass simulations suggest Thar Desert in northwestern India as the primary source of high aerosols dust loading over Jaipur region as well as contribution by long-range transport from the Arabian Peninsula and Middle East gulf regions, during pre-monsoon season.
The current research focuses on the use of different simulation techniques in the future prediction of the crucial aerosol optical properties over the highly polluted Indo-Gangetic Basin in the ...northern part of India. The time series model was used to make an accurate forecast of aerosol optical depth (AOD) and angstrom exponent (AE), and the statistical variability of both cases was compared in order to evaluate the effectiveness of the model (training and validation). For this, different models were used to simulate the monthly average AOD and AE over Jaipur, Kanpur and Ballia during the period from 2003 to 2018. Further, the study was aimed to construct a comparative model that will be used for time series statistical analysis of MODIS-derived AOD
550
and AE
412–470
. This will provide a more comprehensive information about the levels of AOD and AE that will exist in the future. To test the validity and applicability of the developed models, root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), fractional bias (FB), and Pearson coefficient (r) were used to show adequate accuracy in model performance. From the observation, the monthly mean values of AOD and AE were found to be nearly similar at Kanpur and Ballia (0.62 and 1.26) and different at Jaipur (0.25 and 1.14). Jaipur indicates that during the pre-monsoon season, the AOD mean value was found to be highest (0.32 ± 0.15), while Kanpur and Ballia display higher AOD mean values during the winter season (0.72 ± 0.26 and 0.83 ± 0.32, respectively). Among the different methods, the autoregressive integrated moving average (ARIMA) model was found to be the best-suited model for AOD prediction at Ballia based on fitted error (RMSE (0.22), MAE (0.15), MAPE (24.55), FB (0.05)) and Pearson coefficient r (0.83). However, for AE, best prediction was found at Kanpur based on RMSE (0.24), MAE (0.21), MAPE (22.54), FB (-0.09) and Pearson coefficient r (0.82).
The current study discourses the impact of variation in PM
2.5
concentration on the ambient air quality of Delhi. The 24-hourly PM
2.5
concentration dataset was obtained from air quality measurement ...site (Anand Vihar) of Delhi Pollution Control Committee (DPCC) for the duration of April 2015 to December 2018. The annual and seasonal variability in the trend of ambient PM
2.5
along with cumulative impact of meteorological parameters have been analyzed. The overall percentage increase in annual PM
2.5
concentration, compared to National Ambient Air Quality Standards (NAAQS) guidelines, is observed to be 286.09%. The maximum concentration of fine particulate matter was recorded to be 788.6 µg/m
3
during post-monsoon season and it was found to be associated with lower ambient temperature of 21.34°C and wind speed of 0.33 m/sec. Further, PM
2.5
concentration was found to be correlated with CO (
R
= 0.6515) and NH
3
(
R
= 0.6396) indicating similar sources of emission. Further, backward trajectory analysis revealed contribution in PM
2.5
concentration from the states of Punjab and Haryana. The results indicated that particulate pollution is likely to occur in urban atmospheric environments with low temperatures and low wind speeds.
Research highlights
PM
2.5
/PM
10
ratio was observed to be highest in November, December and January, attributing aggravated levels of particle pollution to anthropogenic sources.
Seasonal analysis of PM
2.5
concentration indicated that particulate pollution was severe during post monsoon and winter months.
Carbon monoxide (
R
= 0.6515;
R
2
= 0.4244) and Ammonia (
R
= 0.6396;
R
2
= 0.4088) were found to be correlated with PM
2.5
.
Backward air mass trajectory depicted that air mass direction was coming to the receptor site (Anand Vihar) from the states of Haryana and Punjab.
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