Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand,India

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

     

Attraction of the potential biocontrol agent <Emphasis Type="Italic">Galerucella placida</Emphasis> (Coleoptera: Chrysomelidae) to the volatiles of <Emphasis Type="Italic">Polygonum orientale</Emphasis> (Polygonaceae) weed leaves

Galerucella placida Baly (Coleoptera: Chrysomelidae) is a potential biocontrol agent of the rice-field weed Polygonum orientale L. (Polygonaceae). The volatile organic compound (VOC) profiles from undamaged and mechanically damaged plants, and from plants 12- and 36-h following continuous feeding of female G. placida adults and 2nd instar larvae were identified and quantified by GC–MS and GC-FID analyses. Twenty-four and 21 compounds were identified in volatiles of undamaged and insect feeding plants, respectively; whereas 22 compounds were detected in volatiles of mechanically damaged plants. Decanal and 1-dodecanol were unique to undamaged plants, and linalool was detected in volatiles of undamaged and mechanically damaged plants, but not in volatiles of insect damaged plants. However, the beetles are not attracted by none of these volatile components, when tested individually in Y-shaped glass tube olfactometer bioassays. In all plants, methyl jasmonate was predominant. 1-Undecanol was the least amount in undamaged plants, and plants 12-h after feeding by G. placida adults and larvae; whereas 1-tridecanol was the least abundant in plants 36-h after feeding by G. placida adults and larvae, and mechanically damaged plants. The beetles showed significant preference to the whole volatile blends from plants 12-h after feeding by larvae and plants 36-h after feeding by either larvae or adults compared to those of undamaged plants. Further, G. placida responded to individual synthetic compounds, 3-hexanol, 1-octen-3-ol, nonanal, and geraniol at 7, 1.38, 3.75 and 4.5 µg/25 µL CH₂Cl₂, respectively, and provide a basis for attraction of the potential biocontrol agent in the field.     

Migrants,land markets and carbon emissions in Jambi,Indonesia: Land tenure change and the prospect of emission reduction

Policies designed to reduce land-based carbon emissions require a good understanding of the complex connections between state-sanctioned concessions, forest conversion, informal land markets and migrants. Our case study in the peat forests of the Tanjung Jabung Barat (TanJaBar) regency of Jambi, Indonesia aimed to explore relations between four key stakeholder groups: the state, local communities, migrants, and state-sanctioned concessions. We hypothesized that current land use patterns are shaped by insecurity in formal forest tenure alongside informal land tenure arrangements with migrants. In analyzing the six two-way relationships between the four stakeholder groups, we found that interactions between the stakeholders have changed local norms and practice, causing land conflicts and contested claims that need to be explicitly addressed in efforts to reduce carbon emissions in TanJaBar. Relational concepts of land rights between migrants and local community leaders are informed by social identity, expectations of investment opportunities, insecure customary forest tenure and competing land use policies. Migrants act as intermediaries in shaping the land tenure system and shift the balance of power between local communities, the state, and business concessions. We conclude that effective and equitable implementation of national Reducing Emissions from Deforestation and Forest Degradation+ (REDD+) programs will need to recognize underlying land ownership dynamics, power struggles and strategic positioning among stakeholders across scales. Obtaining free and prior informed consent (FPIC) from all relevant stakeholders is a major challenge given this complexity. Low emission development strategies will require recognition of a reality beyond large-scale concessions and traditional local communities.     

A Multivariate Time Series Approach to Study the Interdependence among O3, NO x , and VOCs in Ambient Urban Atmosphere

A multivariate time series approach vector autoregression (VAR) along with impulse response function and variance decomposition technique has been employed to look into the interrelationship among O₃, NO, NO₂, and volatile organic compounds (VOCs, namely, benzene, ethylbenzene, toluene, and xylene in the present study) using 3 months long continuous time series data of 1 h average concentration of these pollutants at one of the traffic sites in Delhi, India. It is found that the VAR of order 2 (i.e., past two lagged values of 1 h interval) is sufficient to represent the observed time series at the station studied. The impulse response function and variance decomposition analysis indicate that O₃ concentration shows an immediate rise and persists for a longer duration (typically 8–10 h) once the impulse of NO₂, benzene, ethylbenzene, or xylene is given in the ambient environment. However, in case of toluene, the reverse effect has been observed. Since O₃ forms in the troposphere due to photolysis of NO₂, it is not surprising that its impulse triggers O₃ formation in the ambient environment. However, in case of VOCs, this has been attributed to their tendency to show higher inclination toward intermediary reactions leading to the formation of O₃ rather than their (VOCs) reaction with O₃. Among VOCs, only toluene has been observed to show higher inclination toward its reaction with O₃. Apart from this, variance decomposition technique also reveals that the relation of NO with NO₂ is more important than the relation of NO with O₃ creating a conducive atmosphere for O₃ formation in the present scenario. Thus, the multivariate time series approach provides significant insight about the role played by the dominant individual VOCs and NO _x in influencing the O₃ concentration in ambient urban atmosphere whereas a photochemical modeling approach gives an overall view of NO _x and VOCs behavior with respect to O₃ by using the O₃ isopleth technique.     

Effect of human use,season and habitat on ungulate density in Kanha Tiger Reserve,Madhya Pradesh,India

Conservation practitioners require strata specific, seasonal species densities for habitat management. Herein, we use stratified distance sampling in Kanha Tiger Reserve (KTR) with 200 spatial transects and an effort of 1200 km walk in the year 2013. Analysis was done to access (a) impact of human use and (b) effect of habitat and season on ungulate densities in KTR. While a single detection function for each species was used for estimating density within human-restricted core and multiple use buffer of KTR, species-specific seasonal detections were modelled for each habitat. Ungulate biomass was 4.8 times higher in the core area compared with the buffer zone. The core supported a herbivore density and biomass of 50 ± 4.80/km² and 26,806 ± 2573 kg/km², respectively. Chital were found to be most abundant, having a density of 30.1 ± 4.34/km² and contributing 33 % of the biomass with a habitat preference for grasslands (106 ± 39/km²) in summer and winter. Sambar had highest density (15.4 ± 3.34/km²) in bamboo-mixed habitat, in both seasons. Gaur contributed 39 % of the ungulate biomass and showed a seasonal shift in density from sal forests (9.65 ± 3.55/km²) in summer to miscellaneous forests (8.13 ± 1.94/km²) in winter. Barasingha were restricted to grasslands with similar summer and winter densities of 1.56 ± 0.76/km². Chousingha were rare (0.1 ± 0.04/km²), found mostly in miscellaneous forests and plateau grasslands. Grassland and bamboo-mixed forests supported 58 % of the total ungulate biomass. Management for an optimal habitat mosaic that maintains ungulate diversity, addresses the specific needs of endangered species and maximizes ungulate biomass is recommended.     

GARCH modelling in association with FFT–ARIMA to forecast ozone episodes

In operational forecasting of the surface O₃ by statistical modelling, it is customary to assume the O₃ time series to be generated through a homoskedastic process. In the present work, we’ve taken heteroskedasticity of the O₃ time series explicitly into account and have shown how it resulted in O₃ forecasts with improved forecast confidence intervals. Moreover, it also enabled us to make more accurate probability forecasts of ozone episodes in the urban areas. The study has been conducted on daily maximum O₃ time series for four urban sites of two major European cities, Brussels and London. The sites are: Brussels (Molenbeek) (B1), Brussels (PARL.EUROPE) (B2), London (Brent) (L1) and London (Bloomsbury) (L2). Fast Fourier Transform (FFT) has been used to model the periodicities (annual periodicity is especially distinct) exhibited by the time series. The residuals of “actual data subtracted with their corresponding FFT component” exhibited stationarity and have been modelled using ARIMA (Autoregressive Integrated Moving Average) process. The MAPEs (Mean absolute percentage errors) using FFT–ARIMA for one day ahead 100 out of sample forecasts, were obtained as follows: 20%, 17.8%, 19.7% and 23.6% at the sites B1, B2, L1 and L2. The residuals obtained through FFT–ARIMA have been modelled using GARCH (Generalized Autoregressive Conditional Heteroskedastic) process. The conditional standard deviations obtained using GARCH have been used to estimate the improved forecast confidence intervals and to make probability forecasts of ozone episodes. At the sites B1, B2, L1 and L2, 91.3%, 90%, 70.6% and 53.8% of the times probability forecasts of ozone episodes (for one day ahead 30 out of sample) have correctly been made using GARCH as against 82.6%, 80%, 58.8% and 38.4% without GARCH. The incorporation of GARCH also significantly reduced the no. of false alarms raised by the models.     

A Wavelet-based Neural Network Model to Predict Ambient Air Pollutants’ Concentration

The present paper proposes a wavelet based recurrent neural network model to forecast one step ahead hourly, daily mean and daily maximum concentrations of ambient CO, NO₂, NO, O₃, SO₂ and PM_2.5 — the most prevalent air pollutants in urban atmosphere. The time series of each air pollutant has been decomposed into different time-scale components using maximum overlap wavelet transform (MODWT). These time-scale components were made to pass through Elman network. The number of nodes in the network was decided on the basis of the strength (power) of the corresponding input signals. The wavelet network model was then used to obtain one-step ahead forecasts for a period extending from January 2009 to June 2010. The model results for out of sample forecast are reasonably good in terms of model performance parameters such as mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized mean absolute error (NMSE), index of agreement (IOA) and standard average error (SAE). The MAPE values for daily maximum concentrations of CO, NO₂, NO, O₃, SO₂ and PM2.5 were found to be 9.5%, 17.37%, 21.20%, 13.79%, 17.77% and 11.94%, respectively, at ITO, Delhi, India. Bearing in mind that the forecasts are for daily maximum concentrations tested over a long validation period, the forecast performance of the model may be considered as reasonably good. The model results demonstrate that a judicious selection of wavelet network design may be employed successfully for air quality forecasting.     

Practical ways to recycle plastic: current status and future aspects

Journal of Material Cycles and Waste Management - With sky-rocketing demand and unrestricted global production, plastics have become an inseparable part of daily human life and the circular economy...