The response of mangrove (Avicennia marina) seedlings to treated (wet) sludge from a sewage treatment plant (STP) was tested in a randomized block design experiment at a tree nursery on Mubarraz Island in the Arabian Gulf. The growth response of seedlings to half-strength and full-strength STP sludge was monitored over 103 days and compared with the response to freshwater, seawater and half-strength seawater treatments. Sludge treatments resulted in significantly greater plant growth, leaf number, leaf biomass and root biomass than the other treatments did. The positive effect of STP sludge on seedling growth is attributed to enhanced levels of total nitrogen (8.9 ± 0.1 mg l⁻¹) and total phosphorus (7.8 ± 0.2 mg l⁻¹) in the sludge and its low salinity. These results suggest that sludge from sewage treatment plants may be beneficially used in mangrove nurseries and plantations in this arid region, where soils are nutrient-poor and fresh water is scarce.

Atmospheric reactive nitrogen (Nr) pollution leads to enhanced Nr deposition. There still big gaps in understanding atmospheric nitrogen deposition because of limited monitoring sites in arid land ecosystems of Central Asia. To determine Nr concentrations and deposition in the study area, we have set up 20 monitoring sites to collect gaseous, particulate, and precipitation samples and measure their Nr components since 2009. Nr concentrations in air showed large spatial variations. Based on the Nr concentrations, dry deposition was calculated using the monthly average Nr concentrations by the corresponding deposition velocities modeled, which was varied between 3.15 and 27.92 kg N ha⁻¹ yr⁻¹ across desert, grassland, desert-grassland, forest, farmland, and city/suburb ecosystems. Ammonia N deposition varied between 0.50 asnd 8.66 kg N ha⁻¹ yr⁻¹, and nitrate N deposition c varied between 0.67 and 4.22 kg N ha⁻¹ yr⁻¹, respectively, in precipitation. Annual N deposition is following the order of desert (4.0) < grassland (6.0) < desert-grassland (7.6) < forest (16.1) < farmland (18.4) < city/suburb (35.4) ecosystems. Dry deposition contributed 52.7, 53.8, 100, 68.2, 73.7, and 78.9% of total N deposition in grassland, desert-grassland, desert, forest, farmland and city/suburb ecosystems, respectively. Reduced nitrogen deposition accounted for 62% of total N deposition in the arid area. Dry NH₃ deposition made an important contribution (on average 40%) to total N deposition. Therefore, understanding the characteristics of Nr pollution especially NH₃ emission is indispensable to atmospheric pollution control in arid region.

Net primary productivity (NPP) has been widely used as the indicator of vegetation function and exhibits large spatial and temporal variations caused by numerous factors. Northwest China (NWC) is one of the driest regions in China, and water supply is the key determinant of NPP here. However, studies on the effects of water stress on NPP in NWC at the regional scale are still relatively lacking. Thus, in this study, based on a set of Moderate-Resolution Imaging Spectroradiometer (MODIS) NPP and evapotranspiration (ET) datasets, we quantified the response of NPP to water stress, which is indicated by crop water stress index (CWSI). Regional average of annual NPP in NWC showed an increasing trend during the study period, at a rate of 0.84 g C m⁻² yr⁻¹. At the province level, the NPP increase rates increased in the order of Ningxia (7.7%), Shaanxi (6.5%), Gansu (4.5%), Qinghai (3.8%), and Xinjiang (1.7%). NPP was negatively correlated with CWSI (p<0.05) in 73% of areas, indicating the key role of water stress in constraining NPP over this arid region. The effect of water stress on NPP changes with elevation. Water stress has the strongest negative impact on NPP in areas with elevations around 2000 m. In elevations above 5000 m, NPP is not limited by water stress, mostly positively correlated with CWSI. Our findings further clarify the importance of water stress in dryland ecosystems, while highlighting that elevation gradients can significantly affect the correlation between NPP and water stress.

Water scarcity constrains global cotton production. However, partial root-zone drying (PRD) and mulching can be used as good techniques to save water and enhance crop production, especially in arid regions. This study aimed to evaluate the effects of mulching for water conservation in an arid environment under PRD and to further assess the osmotic adjustment and enzymatic activities for sustainable cotton production. The study was carried out for 2 years in field conditions using mulches (NM = no mulch, BPM = black plastic mulch at 32 kg ha⁻¹, WSM = wheat straw mulch at 3 tons ha⁻¹, CSM = cotton sticks mulch at 10 tons ha⁻¹) and two irrigation levels (FI = full irrigation and PRD (50% less water than FI). High seed cotton yield (SCY) achieved in FI+WSM (4457 and 4248 kg ha⁻¹ in 2017 and 2018, respectively) and even in PRD+WSM followed by BPM>CSM>NM under FI and PRD for both years. The higher SCY and traits observed in FI+WSM and PRD+WSM compared with the others were attributed to the improved water use efficiency and gaseous exchange traits, increased hormone production (ABA), osmolyte accumulation, and enhanced antioxidants to scavenge the excess reactive oxygen. Furthermore, better cotton quality traits were also observed under WSM either with FI or PRD irrigation regimes. Mulches applications found effective to control the weeds in the order as BPM>WSM>CSM. In general, PRD can be used as an effective stratagem to save moisture along with WSM, which ultimately can improve cotton yield in the water-scarce regions under arid climatic regions. It may prove as a good adaptation strategy under current and future water shortage scenarios of climate change.

Global solar radiation is the total amount of solar energy received on a horizontal surface and defined as the sum of direct, diffused, and reflected solar radiation. Global solar radiation is an important variable in agricultural, meteorological, hydrological, and climatological studies. The purpose of this paper is to develop an effective method to estimate the daily global solar radiation using different atmospheric properties detected from satellite data, including cloud fraction, cloud optical depth, aerosol optical depth, aerosol exponent, aerosol index, and precipitable water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) and ozone monitoring instrument (OMI) daytime data in the urban area of Mashhad, Iran, during the years from 2000 to 2018. Based on seven combinations of the atmospheric properties, models were developed using a standard statistical method, namely, multiple linear regression method and a specific class of artificial neural networks, namely, feedforward multilayer perceptron. The efficiency of the models was compared for the assessment of the daily global solar radiation based on the combinations of the input data. For both methods, 80% percent of the data are used for model development and the remaining data for validation. Results of pairwise statistics indicate that, on average, the estimates were more accurate using the artificial neural networks than the regression method. Results show that in both methods, the accuracy of estimation improves when cloud fraction is used as a predictor. This implies the significant effect of cloud cover on solar radiation. However, using the cloud optical depth decreases the accuracy of the estimation of global solar radiation, i.e., the least accurate model is the one with cloud fraction and cloud optical depth for the neural network method and the model with CF and AE for the regression method. The estimation error comes from the inaccuracy in measuring cloud optical depth that depends on satellite sensor resolution and the inhomogeneity of types and microphysical properties of clouds over the study area. Due to the arid climate of the study area, the precipitable water vapor content does not considerably affect radiation attenuation. The best estimate is earned by cloud fraction and aerosol index as inputs indicating the simultaneous role of aerosol and cloud in global solar radiation. Aerosol index considers the effect of absorbing aerosols such as black carbon and dust and is a complementary information to the cloud cover. The results imply that both methods have the potential to achieve an operational stage, taking advantage of the better availability of satellite data. Even though the artificial neural network is found to be more accurate than multiple linear regression, using the regression method is recommended because it is more easy to use. Results show that the effective variables vary in different seasons. In both methods, estimation error is highest in the spring and lowest in the fall and winter. The high inaccuracy may be due to the high sensitivity of radiative transfer to atmospheric condition in spring. On the other hand, the high accuracy may be caused by the less solar radiation fluctuations during fall and winter because of the lower solar radiation flux.