Greenspace pattern, meteorology and air pollutant in Taiwan: A multifaceted connection.

Air pollution is a global environmental concern that poses a significant threat to human health. Given the impact of urbanization and climate change, green planning is being encouraged to improve air quality. The study aims to examine the intricate relationships between greenspace pattern and outdoor air around 73 in-situ stations over Taiwan during the dry (November to April) and wet (May to December) seasons from 2015 to 2020. To achieve this, Partial Least Squares - Structural Equation Modeling was utilized to analyze the interactions among seven dimensions: greenspace - GS, gaseous pollutant - GP, particle pollutant - PP, O3 - OZONE, air temperature - TEMP, relative humidity - RH, and wind speed - WS. The GS involves seven landscape metrics: edge density, total edge, effective mesh size, largest patch area, percentage of landscape, total core area, and patch cohesion index. The results indicate that the GS has a stronger effect on the GP, whereas its effect on the PP is weaker during the dry season compared to the wet season. While its effect on the TEMP is weaker, it shows a slightly stronger effect on the RH during the dry season. Moreover, the GS mediates the air pollutant dimensions during the two seasons, with the RH acting as a primary mediator. The meteorological dimensions primarily have a greater influence on the air pollutant dimensions during the dry season than the wet season. Consequently, the GS explains 11.3 % more and 18.4 % less of the variances in the RH and TEMP during the dry season, respectively. Moreover, the GS and meteorological dimensions yield a seasonal difference in explained variance, with the highest value observed for the OZONE (R2 = 24.2 %), followed by the PP (R2 = 9.7 %) and GP (R2 = 7.7 %). Notably, seven landscape metrics serve as potential indicators for green strategies in urban planning to enhance outdoor air quality.

Rising surface ozone due to anthropogenic activities and its impact on COVID-19 related deaths in Delhi, India.

The rapidity and global spread of the COVID-19 pandemic have left several vital questions in the research community requiring coordinated investigation and unique perspectives to explore the relationship between the spread of disease and air quality. Previous studies have focused mainly on the relation of particulate matter concentration with COVID-19-related mortalities. In contrast, surficial ozone has not been given much attention as surface ozone is a primary air pollutant and directly impacts the respiratory system of humans. Hence, we analyzed the relationship between surface ozone pollution and COVID-19-related mortalities. In this study, we have analyzed the variability of various atmospheric pollutants (particulate matter (PM2.5 and PM10), Nitrogen dioxide (NO2), Carbon monoxide (CO), and Ozone) in the National Capital Region (NCR) of India during 2020-2021 using station data and investigated the relationship of the air-quality parameters with the COVID-19 related deaths. In northern parts of India, the concentration of particulate matter (PM2.5 and PM10), Nitrogen dioxide (NO2), Carbon monoxide (CO), and Ozone remain high during the pre- and post-monsoon seasons due to dust loading and crop residue burning (after winter wheat in April & summer rice in November). The westerly wind brings the polluted airmass from western and northwestern parts to Delhi and National Capital Region during April-June and October-November, and meteorological conditions help raise the concentration of these pollutants. Due to long solar hours and high CO concentrations, the ozone concentration is higher from April to June and September. While comparing major air quality parameters with COVID-19-related deaths, we found a good relationship between surface ozone and COVID-19 mortality in Delhi. We also observed a time lag relationship between ozone concentration and mortality in Delhi, so the exposure to Ozone in a large population of Delhi may have augmented the rise of COVID-19-related deaths. The analysis suggested that ozone has a significant relationship with COVID-19 related mortality in Delhi in comparison to other parameters.

Sea surface temperature (SST) and SST anomaly (SSTA) datasets over the last four decades (1977-2016) during typhoon season (May to November) in the entire Global Ocean, North Pacific Ocean, Philippine Sea, South China sea, and Eastern China Sea.

The Sea Surface Temperature Anomalies (SSTA) are created for the chosen study period from 1977 to 2016 (40-years) including the base period from 1941 to 1970 (30-years) using the two different raw Sea Surface Temperature (SST) datasets named Optimum Interpolation (OI) SST version 2 and Centennial in situ Observation-Based Estimates (COBE) SST version 2. The SSTA and SST are measured for each month from May to November (typhoon activity months in the North West Pacific) over the entire Global Ocean, especially focusing on the North Pacific Ocean; Philippine Sea; South China Sea; and Eastern China Sea (the marginal Seas of the North West Pacific Ocean). The OI-SST V2 dataset is directly accessed by the online link https://psl.noaa.gov/, which is made available by the Physical Sciences Laboratory (PSL) of the National Oceanic and Atmospheric Administration (NOAA). OI-SST V2 dataset contains monthly-averaged SST data from December 1981 to May 2020. COBE-SST V2 dataset belongs to the Japan Meteorological Agency (JMA) and is also made available by the PSL of NOAA through the online link https://psl.noaa.gov/. COBE-SST V2 dataset contains a very long period of monthly-averaged SST data from January 1850 to December 2019. The SST data in both datasets are on a regular one-degree (1o) grid covering the entire Oceans of the Earth. Both datasets are in the Network Common Data Form (NetCDF)(.nc) and can be opened on any appropriate software platform like ESRI ArcGIS 10.5 for further analysis. All SST data presented in this article merely belong to the typhoon season months (from May to November) of the North West Pacific (NWP) Ocean basin and are thus crucial for typhoon-related research. At First, the SST data for each month from May to November over the whole study and the base periods are extracted for the entire Global Ocean. Then, for each successive 5-year period and 10-year period, the SST data is averaged separately for each month from May to November. Also, for the whole 40 years of the chosen current period and 30 years of the base period, the SST data is averaged separately for each month of the typhoon season. The successive year, 5-year, and 10-year SST data of the chosen current period is averaged for all seven months of typhoon season. Also, for the whole 40 years of the chosen current period and 30 years of the base period, the SST data is averaged over all seven months of typhoon season. Finally, the yearly, 5-yearly, 10-yearly, and monthly Sea Surface Temperature Anomalies (SSTA) are measured using the chosen current and base period data for the entire Global Ocean, North Pacific Ocean, Philippine Sea, South China sea, and Eastern China Sea. Statistical analyses are done, which are significant for global warming, SST, and typhoon-related research. For detailed analysis, explanation, and discussion, the readers are referred to the "Typhoon strength rising in the past four decades" [1].

Monitoring drought pattern for pre- and post-monsoon seasons in a semi-arid region of western part of India.

Drought has become a regular phenomenon in the western semi-arid regions of India, where severe drought occurs once in 8-9 years. Therefore, two drought indices, namely temperature condition index (TCI) and vegetation condition index (VCI), were prepared from using Landsat datasets to appraise and monitor of drought pattern for the pre- and post-monsoon seasons between 1996 and 2016 in the Latur district, the north-western part of India. Additionally, the average frequency layers (AFL) of all drought and land use indices were prepared to analyse the correlation between them. The results show a substantial increase in the area under high, very high and severe drought classes both pre- and post-monsoon seasons during the study period. The highest increase was noticed from the high drought class from 2532.45 to 4792.49 sq. km and 1559.84 to 3342.32 sq. km for pre- and post-monsoon season, respectively, based on the TCI and 1269.81 to 1787.77 sq. km in very high drought class for the post-monsoon season using the VCI. The correlation analysis showed that there exists a significant relationship between the land use indices and drought indices. However, the spatial pattern of correlation was heterogeneous for both pre- and post-monsoon seasons. The results of this research can help in the drought management and mitigation planning in the study area. In addition, a similar approach may be applied to analyse drought patterns in other places with similar geographic characteristics as both VCI and TCI are cost-effective and less time-consuming methods and produce reliable outcomes.

Physicochemical and biological analysis of river Yamuna at Palla station from 2009 to 2019.

Yamuna is one of the main tributaries of the river Ganga and passes through Delhi, the national capital of India. In the last few years, it is considered one of the most polluted rivers of India. We carried out the analysis for the physiochemical and biological conditions of the river Yamuna based on measurements acquired at Palla station, Delhi during 2009-19. For our analysis, we considered various physicochemical and biological parameters (Dissolved Oxygen (DO) Saturation, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Alkalinity, Total Dissolved Solids (TDS), and Total Coliform. The water stats of river Yamuna at Palla station were matched with Water Standards of India, United Nations Economic Commission for Europe (UNECE), and World Health Organization (WHO). Maximum changes are observed in DO saturation and total coliform, while BOD and COD values are also seen higher than the upper limits. Total alkalinity rarely meets the minimum standards. TDS is found to be satisfactory as per the standard limit. The river quality falls under Class D or E (IS2296), Class III or IV (UNECE), and fails to fulfill WHO standards for water. After spending more than 130 million USD for the establishment of a large number of effluent treatment plants, sewage treatment plants, and common effluent treatment plants, increasing discharges of untreated sewage, partially treated industrial effluents and reduced discharge of freshwater from Hathnikund are causing deterioration in water quality and no major improvements are seen in water quality of river Yamuna.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India.

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 µg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

Mapping global eco-environment vulnerability due to human and nature disturbances.

Identifying vulnerable levels of eco-environment over a global scale is critical for environmental management and ecological conservation. We present the method to optimize the use of freely assessable datasets to derive 16 factors for a proposed assessment framework (Nguyen and Liou, 2019; Liou et al., 2017; Nguyen et al., 2016) [[1], [2], [3]]. Results show that the datasets are suitable for evaluating global eco-environmental vulnerability (GEV). PM2.5 that is a hazardous substance in environment and an anthropogenic disturbance associated with nature and human-made influence is selected to validate the GEV map. The GEV map well correlates with PM2.5 distribution patterns with correlation coefficient of approximately 0.82. All datasets and mapping procedures are processed in ArcGIS 10.3/QGIS 2.16.3 software. Advantages of our method include three aspects: •The analysis procedure is simple but powerful, while dealing with various complex environmental issues.•The framework is flexible to adjust influential indicators subject to the conditions of concerned regions and purposes of decision makers.•The framework can be easily applied for different concerned regions over various scales. Our findings include GEV mapping and eco-protection zoning that provide key hotspots of eco-environmental vulnerability levels over a global scale for the decision makers and people to take further actions to lessen disturbances and achieve environmental sustainability.

Vulnerability of Vietnam to typhoons: A spatial assessment based on hazards, exposure and adaptive capacity.

Typhoons have devastating impacts across many Asian countries. Vietnam is presently one of the most disaster-prone nations. Typhoons regularly disrupt human lives and livelihoods in various ways and cause significant damage. Making efficient policy decisions to minimize the vulnerability of affected communities is crucial. This requires a deep understanding of the factors that make a society vulnerable to extreme events and natural disasters. An appropriate approach is integrating the three dimensions of hazard, exposure and sensitivity, and community adaptive capacity. However, the vulnerability and adaptive capacity response to typhoons within Vietnam is poorly investigated. Here, we develop a conceptual framework that incorporates 21 indicators to identify vulnerability and adaptive capacity (VAC) using geospatial techniques at regional scales, applied over Vietnam. We find large spatial differences in VAC and are able to identify the top-priority regions that need to enhance their adaptation to typhoons. The Southern Coastal area, South East and Red River Delta demonstrate high and very high vulnerability because of their physical features and the intensity of typhoons that frequently cross these parts of Vietnam. The lower Mekong Delta and Northern Coastal areas are vulnerable to typhoon-driven flood threats, in particular where compounded by sea-level rise. Our framework successfully identified the spatial distribution and different levels of VAC within acceptable limits of uncertainty. It can therefore serve as a template to tackle national issues in disaster risk reduction in Vietnam and assist in the development of suitable mitigation strategies to achieve sustainable outcomes.

Global mapping of eco-environmental vulnerability from human and nature disturbances.

Global environments are threatened by intensively natural variation and continuously increased human-made disturbances. Assessment of the global eco-environment vulnerability (global EV or GEV) caused by both natural and human-induced disturbances plays a key role in providing valuable information about ecological and environmental background for designing suitable policy measures to improve and restore environment. We present the first global-scale map of quantified eco-environmental vulnerability by integrating remote sensing, GIS modelling, and global census datasets, employing 16 influential factors across five domains: socioeconomics, land resources, natural hazards, hydrometeorology, and topography. The GEV is classified into six levels, namely very low vulnerability, low vulnerability, medium vulnerability, medium high vulnerability, high vulnerability, and very high vulnerability. At global scale, a small fraction of the globe (10.1%) is strongly (high and very high vulnerability) affected by influential factors. Among continents, the largest fraction of very high vulnerability level is attributed to Asia (74.6%) followed by Africa (19.6%). National-scale analysis shows that China and India are the most vulnerable in Asia and in the world. Our study provides accumulative impacts of manmade and natural disturbances, which are vital for decision makers to set improvement targets on specific areas over local, regional, and global scales, and design and adopt new practices to lessen natural and manmade disturbances on environment, while keeping track of evolution of the other environmental aspects.