Explore the vast range of titles available.

This article investigates the Dust Storm Index (DSI) and its trend using the Mann–Kendall test, across urban areas of Iran on the monthly, seasonally, and annually scales from 2000 to 2018. The results showed that cities located in the humid region, especially Khoram Abad and Avaj, had the lowest DSI values, and the cities located in arid regions, particularly Zabol, Sarakhs, and Zahedan, had the highest DSI values during the study period. On a monthly basis, the positive trends were observed in most cities of Iran in March, October, and August, while the negative trends were mainly observed in Feb, May, and June. Birjand, Torbat Heydariyeh, Saveh, Shiraz, and Kerman showed an increasing trend of DSI in most months of the study period. On a seasonal scale, the autumn and summer DSI changes showed significant positive trends in 18% of the urban environments in Iran. A similar trend was observed for 17% and 15% of study urban areas in the spring and winter, respectively. On an annual scale, the significant upward trends in DSI were observed in 13% while its negative changes were found in 10% of study cities. These results can be useful for decision-makers and managers to take appropriate measures to reduce and control dust events in urban areas that have suffered from the increasing trend of dust events in the past years.

North Korea being one of the most degraded forests globally has recently been emphasizing in forest restoration. Monitoring the trend of forest restoration in North Korea has important reference significance for regional environmental management and ecological security. Thus, this study constructed and analyzed a time-series land use land cover (LULC) map to identify the LULC changes (LULCCs) over extensive periods across North Korea and understand the forest change trends. The analysis of LULC used Landsat multi-temporal image and Random Forest algorithm on Google Earth Engine(GEE) from 2001 to 2018 in North Korea. Through the LULCC detection technique and consideration of the cropland change relation with elevation, the forest change in North Korea for 2001–2018 was evaluated. We extended the existing sampling methodology and obtained a higher overall accuracy (98.2% ± 1.6%), with corresponding kappa coefficients (0.959 ± 0.037), and improved the classification accuracy in cropland and forest cover. Through the change detection and spatial analysis, our research shows that the forests in the southern and central regions of North Korea are undergoing restoration. The sampling method we extended in this study can effectively and reliably monitoring the change trend of North Korea forests. It also provides an important reference for the regional environmental management and ecological security in North Korea.

Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to dependent series and cannot detect non-monotonic trends. Şen-innovative trend analysis method is launched into literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial independence and normal distribution and examines a given time series as equally divided into two sub-series. The Şen multiple innovative trend analysis methodology is improved to detect partial trends on different sub-series, again with equal lengths. Climate change strongly affects hydro-meteorological parameters today compared to the last twenty or thirty years and gives asymmetrical trend change points in hydro-meteorological time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, the Şen innovative trend analysis method is revised to satisfy these requirements (ITA_DL). The new approach compared with the traditional Mann-Kendall (MK) and Şen innovative trend analysis (Şen_ITA) gives successful and consistent results. The ITA_DL gives four monotonic trends on May, July, September, and October rainfall series of Oxford although the MK gives three monotonic trends in the May, July, and December and cannot detect trends on the September and October. In the ITA_DL visual inspection, the December rainfall series does not show an overall or partial trend. The ITA_DL trend results are consistent with the Şen_ITA except for the September rainfall series, although it has different trend slope amounts.

Accurately assessing the variation in the frost-free season (FFS) can provide decision support for improving agricultural adaptability and reducing frost harm; however, related studies were inadequate in terms of the Qinghai–Tibet Plateau (QTP). This study analyzed the spatiotemporal changes in the first frost day in autumn (FFA), last frost day in spring (LFS), FFS length and effective accumulated temperature (EAT) during the 1978–2017 period, and their influences on spring wheat potential yield on the QTP, based on daily climatic data and the methodology of Sen’s slope and correlation analysis. The results showed that the annual average FFA and LFS occurred later and earlier from northwest to southeast, respectively, and both the FFS length and EAT increased. From 1978 to 2017, the average regional FFA and LFS were delayed and advanced at rates of 2.2 and 3.4 days per decade, and the FFS and EAT increased by 5.6 days and 102.7 °C·d per decade, respectively. Spatially, the increase rate of FFS length ranged from 2.8 to 11.2 days per decade throughout the QTP, and it was observed to be larger in northern Qinghai, central Tibet and Yunnan, and smaller mainly in eastern Sichuan and southern Tibet. Correspondingly, the increase rate for EAT ranged from 16.2 to 173.3 °C·d per decade and generally showed a downward trend from north to south. For a one-day increase in the FFS period, the spring wheat potential yield would decrease by 17.4 and 9.0 kg/ha in altitude ranges of <2000 m and 2000–3000 m, but decrease by 24.9 and 66.5 kg/ha in the ranges of 3000–4000 m and >4000 m, respectively. Future studies should be focused on exploring the influence of multiple climatic factors on crop production using experimental field data and model technologies to provide policy suggestions.

An in-depth study of the influence mechanism of oasis land surface temperature (LST) in arid regions is essential to promote the stable development of the ecological environment in dry areas. Based on MODIS, MYD11A2 long time series data from 2003 to 2020, the Mann–Kendall nonparametric test, the Sen slope, combined with the Hurst index, were used to analyze and predict the trend of LST changes in the urban agglomeration on the northern slopes of the Tianshan Mountains. This paper selected nine influencing factors of the slope, aspect, air temperature, normalized vegetation index (NDVI), precipitation (P), nighttime light index (NTL), patch density (PD), mean patch area (AREA_MN), and aggregation index (AI) to analyze the spatial heterogeneity of LST from global and local perspectives using the geodetector (GD) model and multi-scale geo-weighted regression (MGWR) model. The results showed that the average LSTs of the urban agglomeration on the northern slopes of the Tianshan Mountains in spring, summer, autumn, and winter were 31.53 °C, 47.29 °C, 22.38 °C, and −5.20 °C in the four seasons from 2003 to 2020, respectively. Except for autumn, the LST of all seasons showed an increasing trend, bare soil and grass land had a warming effect, and agricultural land had a cooling effect. The results of factor detection showed that air temperature, P, and NDVI were the dominant factors affecting the spatial variation of LST. The interaction detection results showed that the interaction between air temperature and NDVI was the most significant, and the two-factor interaction was more effective than the single-factor effect on LST. The MGWR model results showed that the effects of PD on LST were positively correlated, and the impact of AREA_MN and AI on LST were negatively correlated, indicating that the dense landscape of patches has a cooling effect on LST. Overall, this study provides information for managers to carry out more targeted ecological stability regulations in arid zone oases and facilitates the development of regulatory measures to maintain the cold island effect and improve the environment.

The intellectualization of coal mines provides core technical support for the high-quality development of the coal industry. Intelligent texts, especially intelligent policy documents, play an extremely important role in analyzing the trend of intelligent policies in coal mines. This paper collects more than 50 central and local intelligent coal mine policy texts from recent years. The method of text analysis is a tool used for text mining, and semantic networks are generated; it reflects that the policy mainly focuses on promoting large-scale equipment and platform integration, to promote the overall goal of safe, efficient, and intelligent development of coal mining. By analyzing the high-frequency words of the policy from 2016 to 2022, it reflects that the policy trend mainly goes through the following three stages: firstly, eliminate backward enterprises, encourage coal mine automation and mechanization; then, standardize the basic concept of coal mine intellectualization, carry out the transformation of coal mine intellectualization; and the third stage is to promote the application of key technologies of intellectualization, build intelligent demonstration coal mines and reach the acceptance stage, and promote the further development trend of coal mine intellectualization.

Aim: Change in spring phenology is a sensitive indicator of ecosystem response to climate change, and exerts first-order control on the ecosystem carbon and hydrological cycles. The start of season (SOS) in spring can be estimated from satellite data using different spatiotemporal scales, data sets and algorithms. To address the impacts of these differences on trends of SOS, a Bayesian analysis is applied to investigate the rate of SOS advance and whether that advance has slowed down or changed abruptly over the last three decades. Location: 30°-75° N in the Northern Hemisphere. Methods: We applied four algorithms to three different satellite data sets (AVHRR, Terra-MODIS and SPOT) to obtain an ensemble of SOS estimates. A Bayesian analysis was applied to test different hypotheses of SOS trends. Results: Over the period 1982-2011, SOS is likely (74%) to have experienced a significant advance best described by a linear trend (–1.4 ± 0.6 days decade⁻¹). At hemispheric and continental scales, deceleration or abrupt changes in the SOS trend are unlikely (< 30%) to have occurred. Trend analysis restricted to the last decade suggests no significant SOS advance since 2000. This lack of trend can be explained by large interannual variations of SOS and uncertainties in SOS extraction, in the context of a short-term decadal-period analysis. Spatial analyses show that SOS advance could have slowed down over parts of western North America, and the SOS trend could have abruptly changed over parts of Canada and Siberia. Main conclusions: SOS advance is unlikely to have slowed down or changed abruptly at a hemispheric scale over the last three decades. At a regional scale, SOS advance could have slowed down or abruptly changed due to changes in winter chilling or fire regimes. Trends of SOS derived from different satellites were within the uncertainties of SOS extraction.

Ecosystems in arid areas are under pressure from human activities and the natural environment. Long-term monitoring and evaluation of arid ecosystems are essential for achieving the goal of sustainable development. The Xinjiang Uygur Autonomous Region (Xinjiang) is a typical arid region located in Northwest China with a relatively sensitive ecosystem. Under the support of the Google Earth Engine (GEE) cloud platform’s massive data collection, the remote sensing ecological index (RSEI) from 2000 to 2020, both in summer and spring, is established, and the variation trend of the ecological quality in Xinjiang is evaluated by coefficient of variation (CV), Sen’s slope analysis, Mann–Kendall trend test (M–K test) and Hurst index. In addition, a partial correlation analysis is processed between RSEI and selected climatic factors, including precipitation and temperature, to find out the mode of correlation between ecological quality and the natural climate. In the last two decades the following has become apparent: (1) The RSEI values of Xinjiang have been relatively low and unstable both in summer and spring, with a trend toward increasing; (2) The distribution characteristics of RSEI levels both in summer and spring have been similar; low levels were concentrated in the desert and wilderness, while high levels were concentrated around the oasis; (3) The ecological quality in Xinjiang has been relatively stable, with a trend of sustained increase both in summer and spring. There was also a small area of sustained decrease around the Junggar Basin and Turpan Basin in summer and a small area of significant decrease in the center of the Taklamakan Desert in spring; (4) In summer, the precipitation has obviously positively correlated in the Southwest. The temperature has obviously positively correlated in the northwestern part; in spring, the precipitation has obviously positively correlated in the Western part; the temperature has obviously positively correlated in the oasis around the Yili River Basin and Tarim Basin.

Over the last 71 years, the air temperature in Poland has increased on average by 0.28 °C per decade—which gives a total change in this period exceeding 2 °C. The subject of this study was an analysis of the long-term variability of the Polish climate in terms of thermal characteristics. The aim of the research was to verify the hypothesis on the lack of homogeneity of this change and to identify points of significant acceleration of the observed tendencies. The analysis utilized the average monthly air temperature at selected synoptic stations in Poland over the period 1951–2021. The values were then processed into a reference series using Alexandersson’s method, which provided synthetic information on the variability in thermal conditions in the country. The analyses were carried out on an annual and seasonal basis. The values of the trend coefficients (and their statistical significance) were also calculated in shorter periods (minimum 30 years), which enabled determination of the stability of the observed changes’ tendencies. In addition to the analysis of the basic characteristics, non-parametric tests (Wilcoxon, Kruskal–Wallis) were used to verify shifts between decades. The annual and seasonal analyses showed the existence of sub-periods with different directions and scales of the observed tendencies. Additionally, statistically significant changes in decadal characteristics were noted, e.g., in the decades 2001–2010 and 2011–2020 in the case of annual temperature, and 1961–1970 and 1971–1980 in the case of the winter season.

At the city scale, the diurnal and seasonal variations in the relationship between urban form and the urban heat island effect remains poorly understood. To address this deficiency, we conducted an empirical study based on data from 150 cities in the Jing-Jin-Ji region of China from 2000 to 2015. The results derived from multiple regression models show that the effects of urban geometric complexity, elongation, and vegetation on urban heat island effect differ among different seasons and between day and night. The impacts of urban geometric factors and population density in summer, particularly those during the daytime, are significantly larger than those in winter. The influence of urban area and night light intensity is greater in winter than in summer and is greater during the day than at night. The effect of NDVI is greater in summer during the daytime. Urban vegetation is the factor with the greatest relative contribution during the daytime, and urban size is the dominant factor at night. Urban geometry is the secondary dominant factor in summer, although its contribution in winter is small. The relative contribution of urban geometry shows an upward trend at a decadal time scale, while that of vegetation decreases correspondingly. The results provide a valuable reference for top-level sustainable urban planning.