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In light of the macro downburst's ground divergent flow field characteristics and high reflectivity, this paper proposes an algorithm for identifying the downburst area using a Doppler weather radar low-level radial velocity and reflectivity factor (abbreviated as reflectivity, the same below). To binarize the radial velocity, perform quality control on the radial velocity and reflectivity, then combine the reflectivity and the radial velocity threshold. Following that, use the Eight-Neighborhood method to retrieve the positive and negative velocity connected regions and perform the connected regions. The positive and negative velocity pairs are then matched, and the zero Doppler velocity line between the positive and negative velocity pairs is extracted, followed by the center recognition of the positive and negative velocity downburst areas. The data of downbursts detected by Doppler radar in Jinan, Shandong Province, are used for algorithm verification in this paper. The results show that the proposed algorithm can detect the macro downburst area and identify the downburst center. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the Seq2Seq model understands each predictor variable. The hybrid model combining an RF with Seq2Seq network (RF-S2S) was evaluated and achieved a Pearson's correlation coefficient of 0.93, a Nash–Sutcliffe coefficient (NS) of 0.79, a Root Mean Square Error (RMSE) of 5.85 µg/m3, a Mean Absolute Error (MAE) of 4.50 µg/m3, and a Mean Absolute Percentage Error (MAPE) of 20.86%. Feature selection by an RF model improves the performance of the Seq2Seq model, reducing errors by 19.7% (RMSE), 20.3% (MAE), and 29.3% (MAPE), respectively. Carbon monoxide (CO) and PM10 are two common, important features influencing the prediction of NO2 concentrations in coastal areas of northern China. The results of RF-S2S models can capture general trends and disruptions more accurately than can long-short term memory (LSTM) models with and without feature selection. The decreasing tendency of NO2 from 2014 to 2020 illustrated by the empirical mode decomposition (EMD) method is one important obstacle to improving the RF-S2S prediction accuracy. An EMD-based RF-S2S model could help to perform the short-term forecast of NO2 concentrations efficiently. [ABSTRACT FROM AUTHOR]

The North Yellow Sea, located at the intersection of the Eurasian continent and North Pacific Ocean at mid-latitudes, is a sensitive area subjected to the joint actions of the ocean, land, and monsoons. On its southern shore, loess and paleosol sedimentary sequences were widely developed during the last interglacial period, which is of great significance for revealing patterns of climate change and dynamic conditions. In this paper, we focus on the Zhifu section (ZFS) on Zhifu Island within the Shandong Province of China. The optically stimulated luminescence (OSL) dating method was used to construct our chronological framework. Grain size and its endmember (EM) components were then analyzed; EM1 is a clay component EM, which represents a weak dynamic environment and strong weathering pedogenesis, while EM2 and EM3 are silt and very fine sand component EMs, respectively, representing a strong dynamic environment and weak weathering pedogenesis. Maximum EM1, mean grain size, clay content, and pH values occur in the paleosol layers (ZF4, ZF6, and ZF8), with minimum values in the loess layers (ZF5 and ZF7); EM3 values show the opposite pattern. This indicates that the ZF4, ZF6, and ZF8 layers represent warm and humid environments with abundant precipitation, where the East Asian summer monsoon (EASM) was enhanced, corresponding to Marine Isotope Stages (MIS) 5a, 5c, and 5e. In contrast, ZF5 and ZF7 represent sub-warm and humid environments with less precipitation, where the EASM was weakened, corresponding to MIS 5b and 5d. Among these stages, MIS5e is the warmest and wettest. These climatic events reveal the pattern of climate fluctuation over a ten-thousand-year timescale; they are synchronous with climate changes recorded in other geological repositories, such as cave stalagmites in southern China and sea-level fluctuations in the Yellow-Bohai Sea, which result from changes in global solar radiation. [ABSTRACT FROM AUTHOR]

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities. [ABSTRACT FROM AUTHOR]

Food security plays an important role in maintaining national stability and sustainable development of human society, and its research has become a hot issue at present. Shandong is the main grain producing area in China, and its grain production plays an important role in national food security. Accordingly, this paper is based on the county climate change, grain yield, sown area, fertilizer use, total power of rural machinery, and total population data in Shandong Province from 1995 to 2020. The evolution process of the food security pattern was studied by the methods of spatial analysis and comprehensive evaluation, the influencing factors of food security were quantitatively analyzed, and the adaptive countermeasures to alleviate the food security risks in this region were discussed. The results show that: Grain production increased by 30.62% from 1995 to 2020. The total population and per capita food availability also increased. Since 2000, more than a quarter of counties have experienced a high risk of food insecurity. The spatial agglomeration of grain production was enhanced, and the local agglomeration characteristics were significantly different. The average temperature in the growing season, the sown area, and the total power of agricultural machinery had a significant positive impact on grain production, while the annual average temperature had a significant negative impact on grain production. Improving the food supply system, strengthening the protection of cultivated land, improving the efficiency of fertilizer utilization, and increasing investment in agricultural science and technology can effectively alleviate food security risks. [ABSTRACT FROM AUTHOR]

On 12–13 February 2016, a record-breaking rain–snow event during the passage of an extratropical cyclone occurred in Shandong Province, China, in which the 24 h precipitation totals at 48 of 123 national meteorological stations in Shandong Province broke their historical records for the month of February, and a further 25 stations recorded their second-largest February totals. This paper investigates the evolution of the mesoscale precipitation areas and the mechanisms responsible for the formation, organization, and maintenance of the mesoscale precipitation areas, using FY-2G satellite data, Doppler radar observations, and a Weather Research and Forecasting (WRF) model numerical simulation at 4 km grid spacing. The main results show that the comma head cloud of the cyclone developed from four echo strips. Intense precipitation was related to the mesoscale elongated precipitation areas (EPAs) of reflectivity >30 dBZ within the stratiform clouds. The formation and development of the EPAs coincided with the activities of a low-level shear line and an associated increase in frontogenesis. The simulated EPAs occurred in an environment of conditional instability (CI), inertial instability (II), and conditional symmetric instability (CSI). In the initial stage of the elongated rainfall areas (ERAs), rainfall was initiated by the frontal forcing in the presence of elevated CI, and II was generated by upright convection. During the development stage of the ERAs, the CI was absent, and condensational heating was enhanced. II occurred in the absence of upright convection, and it seems likely that the presence of II is a diabatic signature of the precipitation itself. Upper-level II intensified the convective systems by enhancing outflow aloft, and II caused the ERAs to organize. Thus, II played an important role in the organization and maintenance of the ERAs. The frontogenesis provided the dynamic condition for the release of the instability. Enhanced CSI and II intensified slantwise convection, and combining with enhanced frontogenesis, intensified the ERAs. The echo, ascent, and frontogenesis in snowfall areas were weaker than those in rainfall areas. [ABSTRACT FROM AUTHOR]

This study utilizes data from national ground meteorological observation stations in Shandong province, Fengyun-4 satellite data, and ERA5 reanalysis data. Through the calculation of atmospheric heat source changes, the role of diabatic heating in the occurrence and development of heavy rainfall is revealed. The widespread heavy-to-torrential rainfall event in Shandong province on 25 June 2018 is analyzed as a case study. It was found that a deep and robust southwest jet stream was the key system for the formation of this rainfall event. Satellite cloud images during the peak rainfall period showed vigorous development in the rainfall cloud region. During the concentrated rainfall period and when the low-altitude jet stream strengthened, there was mostly cold advection overhead at the observation station. The low-altitude jet stream transported moisture, increasing the humidity gradient, thus enhancing frontogenesis. The warm advection in the low-altitude jet stream was not the main energy supplier during heavy rainfall, and local temperature variations were the primary contributors to the thermodynamic conditions during the peak rainfall period. The rate of warming caused by the condensation and release of heat from water vapor significantly increased during the concentrated rainfall period. This warming effect played a heating role in the middle and lower layers, and the positive feedback from the latent heat release of water vapor condensation intensified the weather system affecting the rainfall, providing strong thermodynamic and dynamic conditions for heavy rainfall. [ABSTRACT FROM AUTHOR]

Background: The occurrence of compound hot extreme (CHE) events in China is increasingly frequent. This study aimed to investigate the association between CHEs and all-cause mortality in Shandong Province and to estimate the attributable excess deaths. Methods: We collected daily data on weather, air pollution, and all-cause mortality at the subdistrict level in Shandong Province from 2013 to 2018. A CHE was defined as both daily maximum and minimum temperatures being higher than their historical 90th percentiles during 2013–2018 hot seasons. A case time-series analysis with a distributed lagged non-linear model was applied to analyze the subdistrict-specific association between different hot extremes and mortality risk, which were then pooled at the province level using meta-analysis. Results: Hot nights (RR = 1.44, 95%CI: 1.35–1.53) and CHEs (RR = 1.77, 95%CI: 1.64–1.90) were significantly associated with an increased mortality risk. CHEs had a greater effect for females (RR = 1.99, 95%CI: 1.81–2.19) and the elderly (>74 years) (RR = 2.14, 95%CI: 1.93–2.38) than their counterparts, respectively. Cardiovascular and respiratory deaths were more susceptible to CHEs than other deaths. Each year, 4888 (95%CI: 4133–5811) excess deaths in Shandong Province were attributable to CHEs, accounting for 2.60% (95%CI: 2.20–3.10%) of all-cause deaths and equating to 50 (95%CI: 42–58) deaths per 1,000,000 residents. The CHE-related mortality burden varied across subdistricts, with the highest occurring in the southeastern area and the lowest occurring in the northeastern and southwestern regions. Conclusion: CHEs and hot nights were substantially associated with excess deaths in Shandong Province, especially for females, the elderly, and residents living in the southeastern area. Our findings may facilitate the development of a heat alert warning system and preventive measures for vulnerable populations. [ABSTRACT FROM AUTHOR]

Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature Tm was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations. [ABSTRACT FROM AUTHOR]

While air quality in China has improved significantly in recent years, the population is becoming increasingly vulnerable to air pollution due to the aging population. In this study, we assessed premature deaths attributable to long- and short-term exposures to PM2.5 and O3, as well as their driving forces in Shandong from 2014 to 2060 based on county-level near-real-time air pollutant concentration datasets and projected concentrations of PM2.5 and O3. We found that the concentrations of PM2.5 and O3 in most districts and counties from Shandong are still higher than the corresponding concentration limit of Grade II. Premature mortality caused by long- and short-term exposures to PM2.5 decreased by 13,045 and 8092 in 2020 compared with those in 2014, respectively. Furthermore, premature mortality attributable to short-term exposure to O3 was 36.08% higher than that due to short-term exposure to PM2.5 in 2020. The results of the driving force analysis indicate that the health benefits brought about by the improvement in air quality have been offset by the changes in population age structure. In the future, the increase in O3 concentration and population aging are the top two driving forces having adverse effects on the health burden. This study provides support for controlling the health risks of PM2.5 and O3 pollution, especially for the development of dual-pollutant concentration targets and synergistic control strategies. [ABSTRACT FROM AUTHOR]

Among the China Seas, the Yellow Sea has the highest occurrence frequency of sea fog that can be categorized into widespread sea fog and coastal sea fog. In this paper, we study a typical coastal sea fog along the Shandong Peninsula that is accompanied by sea-land breezes. Based on a series of numerical experiments conducted by the Weather Research and Forecasting model with high spatial resolution, the impacts of the sea–land breezes on the formation and development of sea fog are investigated in detail. The land breeze can act as an accelerator that enhances humidification and cooling near the coast which promotes sea fog formation during the nighttime, resulting from nearshore vapor convergence and offshore cool-air transportation jointly by the descending branch and low-level breeze of the land breeze circulation. During the daytime, the sea breeze acts as a reducer that inhibits sea fog development, including the contraction of fog area caused by onshore advection of the sea breeze on cloud liquid water, and the restraint of fog vertical growth due to warming and drying by the descending branch of the sea breeze circulation. [ABSTRACT FROM AUTHOR]

In the process of rapid urbanization, air environment quality has become a hot issue. Aerosol optical depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) can be used to monitor air pollution effectively. In this paper, the Spearman coefficient is used to analyze the correlations between AOD and urban development, construction factors, and geographical environment factors in Shandong Province. The correlation between AOD and local climatic conditions in Shandong Province is analyzed by geographic weight regression (GWR). The results show that in the time period from 2007 to 2017, the AOD first rose and then fell, reaching its highest level in 2012, which is basically consistent with the time when the national environmental protection decree was issued. In terms of quarterly and monthly changes, AOD also rose first and then fell, the highest level in summer, with the highest monthly value occurring in June. In term of the spatial distribution, the high-value area is located in the northwestern part of Shandong Province, and the low-value area is located in the eastern coastal area. In terms of social factors, the correlation between pollutant emissions and AOD is much greater the correlations between AOD and population, economy, and construction indicators. In terms of environmental factors, the relationship between digital elevation model (DEM), temperature, precipitation, and AOD is significant, but the regulation of air in coastal areas is even greater. Finally, it was found that there are no obvious differences in AOD among cities with different development levels, which indicates that urban development does not inevitably lead to air pollution. Reasonable development planning and the introduction of targeted environmental protection policies can effectively alleviate pollution-related problems in the process of urbanization. [ABSTRACT FROM AUTHOR]

In order to reveal the correlation between aerosols and pollution indicators, the MODIS aerosol optical depth (AOD) was used to investigate the distribution of AOD in 16 prefecture-level cities in Shandong Province from 2015 to 2020. This study quantitatively analyzed the coupling degree and the coupling coordination degree between AOD and pollution indicators based on the coupling coordination model. The results showed that: (1) The annual average AOD in Shandong Province showed a rapid downward trend with a mean value of 0.615. The seasonal AOD of Shandong Province and prefecture-level cities was characterized by spring and summer > autumn and winter. The distribution of AOD in Shandong Province showed a spatial pattern of high in the west and low in the east, and high in the surrounding area and low in the middle. The decreasing rate of AOD was high in the west and low in the east. (2) The annual average AOD and Air Quality Index (AQI) were in a highly coupled and coordinated state. Their spatial distribution pattern decreased from west to east. There were certain fluctuations with seasonal changes, with the largest fluctuation in winter. (3) Except for O3, the overall coupling and coordination level between AOD and each pollutant was relatively high. The coupling coordination effect was as follows: C (PM2.5, AOD) and C (PM10, AOD) > C (NO2, AOD) > C (SO2, AOD), and C (CO, AOD) > C (O3, AOD). Except for the O3, its distribution was characterized by highs in the west and lows in the east. The degree of coupling between each pollution indicator and the seasonal average AOD was high. The study showed that there was a high degree of coupling and coordination between pollutant concentration indicators and AOD, and remote sensing AOD data can be used as an effective supplement to regional pollutant monitoring indicators. [ABSTRACT FROM AUTHOR]

In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOX, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter. [ABSTRACT FROM AUTHOR]

A series of carbon dioxide (CO2) emission inventories with high spatial resolutions covering China have been developed in the last decade, making it possible to assess not only the anthropogenic emissions of large administrational units (countries; provinces) but also those of small administrational units (cities; counties). In this study, we investigate three open-source gridded CO2 emission inventories (EDGAR; MEIC; PKU-CO2) and two statistical data-based inventories (CHRED; CEADs) covering the period of 2000–2020 for 16 prefecture-level cities in Shandong province in order to quantify the cross-inventory uncertainty and to discuss potential reasons for it. Despite ±20% differences in aggregated provincial emissions, all inventories agree that the emissions from Shandong increased by ~10% per year before 2012 and that the increasing trend slowed down after 2012, with a quasi-stationary industrial emission proportion being observed during 2008–2014. The cross-inventory discrepancies increased remarkably when downscaled to the city level. The relative differences between two individual inventories for half of the cities exceeded 100%. Despite close estimations of aggregated provincial emissions, the MEIC provides relatively high estimates for cities with complex and dynamic industrial systems, while the CHRED tends to provide high estimates for heavily industrial cities. The CHRED and MEIC show reasonable agreement regarding the evolution of city-level emissions and the city-level industrial emission ratios over 2005–2020. The PKU-CO2 and EDGAR failed to capture the emissions and their structural changes at the city level, which is related to their point-source database stopping updates after 2012. Our results suggest that cross-inventory differences for city-level emissions exist not only in their aggregated emissions but also in their changes over time. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of "high–high" and "low–low". The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective. [ABSTRACT FROM AUTHOR]

China is experiencing severe PM2.5 (fine particles with a diameter of 2.5 μg or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM2.5 concentration variation in China during the period 1999–2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM2.5 concentration during the last 18 years in the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM2.5 pollution, we find that Tarim Basin endures a high PM2.5 concentration, and this should have some relationship with oil exploration. The relationship between PM2.5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled. [ABSTRACT FROM AUTHOR]