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The transformation of production mode leads to the need to strictly ensure the order in-time delivery. The global control ability of manufacturing workshops has become a necessary capability for enterprises. The real-time accurate Order Remaining Completion Time (ORCT) prediction allows managers to master the production schedule fluctuation, which provides criteria for workshop management. Due to the widespread deployment of Internet of Things (IoT) devices in the workshop, the difficulty of systematic processing and analysis of multi-dimensional heterogeneous data has been the main pain point of ORCT prediction. To tackle these problems, a Manufacturing Big Data (MBD)-driven ORCT prediction method based on Salp Swarm Algorithm (SSA) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. An attribute selection algorithm combined with max-relevance and min-redundancy and regularisation is used to mine key data from MBD. An SSA-BiLSTM model is proposed to achieve efficient and accurate ORCT prediction. The prediction model is supported by BiLSTM, and the hyperparameters are optimised by SSA. Finally, a case study about ORCT prediction in the IoT-enabled manufacturing workshop is presented. The result verifies that the proposed ORCT prediction method has obvious advantages over the other three traditional methods in accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Background: Wood Harvesting and Storage (WHS) is a form of Biomass Carbon Removal and Storage (BiCRS) that utilizes a combined natural and engineered process to harvest woody biomass and put it into long term storage, most frequently in the form of subterranean burial. This paper aims to quantify the availability of woody biomass for the purposes of WHS in the continental United States using a carbon cycle modeling approach. Using a regional version of the VEGAS terrestrial carbon cycle model at 10 km resolution, this paper calculates the annual woody net primary production in the continental United States. It then applies a series of constraints to exclude woody biomass that is unavailable for WHS. These constraints include fine woody biomass, current land use, current wood utilization, land conservation, and topographical limitations. These results were then split into state by state and regional totals. Results: In total, the model projects the continental United States could produce 1,274 MtCO2e (CO2 equivalent) worth of coarse woody biomass annually in a scenario with no anthropogenic land use or constraints. In a scenario with anthropogenic land use and constraints on wood availability, the model projects that 415 MtCO2e of coarse woody biomass is available for WHS annually. This is enough to offset 8.5% of the United States' 2020 greenhouse gas emissions. Of this potential, 20 MtCO2e is from the Pacific region, 77 MtCO2e is from the Western Interior, 91 MtCO2e is from the Northeast region, and 228 MtCO2e is from the Southeast region. Conclusion: There is enough coarse woody biomass available in the continental United States to make WHS a viable form of carbon removal and storage in the country. There is coarse woody biomass available across the continental United States. All four primary regions analyzed have enough coarse woody biomass available to justify investment in WHS projects. [ABSTRACT FROM AUTHOR]

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7–4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from −0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from −1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0–3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm. [ABSTRACT FROM AUTHOR]

Background: Air pollution in China has raised great concerns due to its adverse effects on air quality, human health, and climate. Emissions of air pollutants (APs) are inherently linked with CO2 emissions through fossil-energy consumption. Knowledge of the characteristics of APs and CO2 emissions and their relationships is fundamentally important in the pursuit of co-benefits in addressing air quality and climate issues in China. However, the linkages and interactions between APs and CO2 in China are not well understood. Results: Here, we conducted an ensemble study of six bottom-up inventories to identify the underlying drivers of APs and CO2 emissions growth and to explore their linkages in China. The results showed that, during 1980–2015, the power and industry sectors contributed 61–79% to China's overall emissions of CO2, NOx, and SO2. In addition, the residential and industrial sectors were large emitters (77–85%) of PM10, PM2.5, CO, BC, and OC. The emissions of CH4, N2O and NH3 were dominated by the agriculture sector (46–82%) during 1980–2015, while the share of CH4 emissions in the energy sector increased since 2010. During 1980–2015, APs and greenhouse gases (GHGs) emissions from residential sources generally decreased over time, while the transportation sector increased its impact on recent emissions, particularly for NOx and NMVOC. Since implementation of stringent pollution control measures and accompanying technological improvements in 2013, China has effectively limited pollution emissions (e.g., growth rates of –10% per year for PM and –20% for SO2) and slowed down the increasing trend of carbon emissions from the power and industrial sectors. We also found that areas with high emissions of CO, NOx, NMVOC, and SO2 also emitted large amounts of CO2, which demonstrates the possible common sources of APs and GHGs. Moreover, we found significant correlations between CO2 and APs (e.g., NOx, CO, SO2, and PM) emissions in the top 5% high-emitting grid cells, with more than 60% common grid cells during 2010–2015. Conclusions: We found significant correlation in spatial and temporal aspects for CO2, and NOx, CO, SO2, and PM emissions in China. We targeted sectorial and spatial APs and GHGs emission hot-spots, which help for management and policy-making of collaborative reductions of them. This comprehensive analysis over 6 datasets improves our understanding of APs and GHGs emissions in China during the period of rapid industrialization from 1980 to 2015. This study helps elucidate the linkages between APs and CO2 from an integrated perspective, and provides insights for future synergistic emissions reduction. [ABSTRACT FROM AUTHOR]

Atmospheric inversion of carbon dioxide (CO 2) measurements to better understand carbon sources and sinks has made great progress over the last 2 decades. However, most of the studies, including a four-dimensional variational ensemble Kalman filter and Bayesian synthesis approaches, directly obtain only fluxes, while CO 2 concentration is derived with the forward model as part of a post-analysis. Kang et al. (2012) used the local ensemble transform Kalman filter (LETKF), which updates the CO 2 , surface carbon flux (SCF), and meteorology fields simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this data assimilation system faces the challenge of maintaining carbon mass conservation. To overcome this shortcoming, here we apply a constrained ensemble Kalman filter (CEnKF) approach to ensure the conservation of global CO 2 mass. After a standard LETKF procedure, an additional assimilation is used to adjust CO 2 at each model grid point and to ensure the consistency between the analysis and the first guess of the global CO 2 mass. Compared to an observing system simulation experiment without mass conservation, the CEnKF significantly reduces the annual global SCF bias from ∼ 0.2 to less than 0.06 Gt and slightly improves the seasonal and annual performance over tropical and southern extratropical regions. We show that this system can accurately track the spatial distribution of annual mean SCF. And the system reduces the seasonal flux root mean square error from a priori to analysis by 48 %–90 %, depending on the continental region. Moreover, the 2015–2016 El Niño impact is well captured with anomalies mainly in the tropics. [ABSTRACT FROM AUTHOR]

Background: The CO2 released by humans and livestock through digestion and decomposition is an important part of the urban carbon cycle, but is rarely considered in studies of city carbon budgets since its annual magnitude is usually much lower than that of fossil fuel emissions within the boundaries of cities. However, human and livestock respiration may be substantial compared to fossil fuel emissions in areas with high population density such as Manhattan or Beijing. High-resolution datasets of CO2 released from respiration also have rarely been reported on a global scale or in cities globally. Here, we estimate the CO2 released by human and livestock respiration at global and city scales and then compare it with the carbon emissions inventory from fossil fuels in 14 cities worldwide. Results: The results show that the total magnitude of human and livestock respiration emissions is 38.2% of the fossil fuel emissions in Sao Paulo, highest amongst the 14 cities considered here. The proportion is larger than 10% in cities of Delhi, Cape Town and Tokyo. In other cities, it is relatively small with a proportion around 5%. In addition, almost 90% of respiratory carbon comes from urban areas in most of the cities, while up to one-third comes from suburban areas in Beijing on account of the siginificant livestock production. Conclution: The results suggest that the respiration of human and livestock represents a significant CO2 source in some cities and is nonnegligible for city carbon budget analysis and carbon monitoring. [ABSTRACT FROM AUTHOR]

Simple Summary: After the collapse of the Soviet Union, changes in the agricultural structure led to widespread abandonment of cropland and natural vegetation restoration in Russia, Ukraine, and Belarus. In consequence, corresponding changes in the terrestrial carbon cycle need to be quantified. We simulated this process using a dynamic vegetation model, and found that the conversion of cropland to natural vegetation generally formed a significant carbon sink at 0.99 GtC; the growth of the vegetation carbon pool, especially, was significantly higher than that of soil carbon pool. Land use and cover changes (LUCC) have a fundamental impact on the terrestrial carbon cycle. The abandonment of cropland as a result of the collapse of the Soviet Union offers a typical case of the conversion from cropland to natural vegetation, which could have a significant effect on the terrestrial carbon cycle. Due to the inaccuracy of LUCC records, the corresponding impact on the terrestrial carbon cycle has not been well quantified. In this study, we estimated the carbon flux using the Vegetation-Global-Atmosphere-Soil (VEGAS) model over the region of Russia, Belarus and Ukraine during 1990–2017. We first optimized the LUCC input data by adjusting the Food and Agriculture Organization (FAO) data by Russian statistical data and redistributing the spatiotemporal input data from the Historical Database of the Global Environment (HYDE) to the original model. Between 1990 and 2017, the area of cropland abandonment was estimated to be 36.82 Mha, compared to 11.67 Mha estimated by FAO. At the same time, the carbon uptake from the atmosphere to the biosphere was 9.23 GtC (vs fixed cropland 8.24 and HYDE 8.25 GtC) during 1990–2017, which means by optimizing the cropland distribution data, the total carbon absorption during the abandonment process increased by 0.99 GtC. Meanwhile, the growth of the vegetation carbon pool was significantly higher than that of the soil carbon pool. Therefore, we further highlight the importance of accurate cropland distribution data in terrestrial carbon cycle simulation. [ABSTRACT FROM AUTHOR]

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO 2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r>0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop + SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop + SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO 2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO 2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO 2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop + SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products. [ABSTRACT FROM AUTHOR]

Atmospheric inversion of carbon dioxide (CO2) measurements to understand carbon sources and sinks has made great progress over the last two decades. However, most of the studies, including four-dimension variational (4D-Var), Ensemble Kalman filter (EnKF), and Bayesian synthesis approaches, obtains directly only fluxes while CO2 concentration is derived with the forward model as post-analysis. Kang et al. (2012) used the Local Ensemble Transform Kalman Filter (LETKF) that updates the CO2, surface carbon fluxes (SCF), and meteorology field simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this system faces the challenge of maintaining global carbon mass. To overcome this shortcoming, here we introduce a Constrained Ensemble Kalman Filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation process is applied to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of global CO2 mass. In the context of observing system simulation experiments (OSSEs), we show that the CEnKF can significantly reduce the annual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton by comparing between experiments with and without it. Moreover, the annual bias over most continental regions is also reduced. At the seasonal scale, the improved system reduced the flux root-mean-square error from priori to analysis by 48-90 %, depending on the continental region. Moreover, the 2015-2016 El Nino impact is well captured with anomalies mainly in the tropics. [ABSTRACT FROM AUTHOR]

Precipitation, a key determinant of soil moisture variations, plays an important role in regulating terrestrial carbon fluxes on multiple time scales. It is a critical meteorological forcing to drive terrestrial biosphere model (TBM), however, with a large uncertainty itself. We here investigated to what extent precipitation alone can cause uncertainties of model‐simulated carbon flux from terrestrial ecosystems to atmosphere (FTA), based on eight precipitation products and a TBM, VEGAS. We find that the pattern of uncertainties in simulated FTA obviously differs from the pattern of discrepancies in precipitation, owing to divergent water sensitivities of vegetation over different regions. Globally, the uncertainty in FTA can be up to approximately 40.73% of the uncertainty in TRENDYv6 multi‐model simulated FTA which is caused by model structural and parameter uncertainty. A good linear relationship emerges between global area‐averaged land climatological annual precipitation and simulated total FTA with the slope of −0.0040 PgC yr−1 per mm yr−1 (p = 0.03; negative for carbon sink), where 70% is explained by the sensitivity over extra‐tropical Northern Hemisphere (NH). For seasonal cycle, compared to nearly constant inter‐precipitation spreads over tropics plus extra‐tropical southern hemisphere (Trop + SH), uncertainties in corresponding simulated FTA show obvious seasonal differences with the relatively larger uncertainties in March‐April‐May (MAM) and August‐September‐October (ASO). For interannual variability, uncertainties in simulated total FTA are, albeit smaller, nonnegligible, which are 40.61% (global), 38.17% (Trop + SH), and 29.63% (NH) of the TRENDYv6 inter‐model uncertainty, respectively. Therefore, generating better global precipitation product is important for reducing the uncertainty in simulating terrestrial carbon sinks. Plain Language Summary: Precipitation, a key forcing in terrestrial biosphere model (TBM), has a large uncertainty itself. We here used eight land precipitation products to run TBM VEGAS and explored the resulting uncertainties in simulating carbon flux from terrestrial ecosystems to atmosphere (FTA) on climatology, seasonal cycle, and interannual variability. In climatology, the uncertainty in global FTA induced by different precipitation can be approximately 40.73% of the uncertainty in TRENDYv6 multi‐model simulations induced by model structural and parameter uncertainty. Furthermore, we find a good linear relationship between global area‐averaged land climatological annual precipitation and simulated total FTA, implying more precipitation over land with stronger carbon sinks, especially over extra‐tropical Northern Hemisphere (NH). For seasonal cycle, uncertainties in simulated FTA over tropics plus extra‐tropical southern hemisphere (Trop + SH) show relatively larger values in March‐April‐May and August‐September‐October. For interannual variability, uncertainties in simulated total FTA are up to 40.61% (global), 38.17% (Trop + SH), and 29.63% (NH) of the TRENDYv6 inter‐model uncertainty, respectively. In general, uncertainty in land precipitation data sets can cause considerable uncertainties in simulating FTA, suggesting the importance of generating the better precipitation product for better simulating the terrestrial carbon sinks. Key Points: Uncertainties in simulating land‐atmosphere carbon fluxes (FTA) on multiple time scales induced by different precipitation are investigatedUncertainties in climatology and interannual variability of global total FTA are about 41% of uncertainties in TRENDYv6 simulationsA good linear relationship emerges between global area‐averaged land climatological annual precipitation and simulated total FTA [ABSTRACT FROM AUTHOR]

Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982-2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982-2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. [ABSTRACT FROM AUTHOR]

To prevent the spread of the COVID-19 epidemic, restrictions such as "lockdowns" were conducted globally, which led to a significant reduction in fossil fuel emissions, especially in urban areas. However, CO 2 concentrations in urban areas are affected by many factors, such as weather, biological sinks and background CO 2 fluctuations. Thus, it is difficult to directly observe the CO 2 reductions from sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the restrictions, to determine the reduction signals. We conducted six series of on-road CO 2 observations in Beijing using mobile platforms before (BC), during (DC) and after (AC) the implementation of COVID-19 restrictions. To reduce the impacts of weather conditions and background fluctuations, we analyze vehicle trips with the most similar weather conditions possible and calculated the enhancement metric, which is the difference between the on-road CO 2 concentration and the "urban background" CO 2 concentration measured at the tower of the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. The results showed that the DC CO 2 enhancement was decreased by 41 (±1.3) parts per million (ppm) and 26 (±6.2) ppm compared to those for the BC and AC trips, respectively. Detailed analysis showed that, during COVID-19 restrictions, there was no difference between weekdays and weekends during working hours (09:00–17:00 local standard time; LST). The enhancements during rush hours (07:00–09:00 and 17:00–20:00 LST) were almost twice those during working hours, indicating that emissions during rush hours were much higher. For DC and BC, the enhancement reductions during rush hours were much larger than those during working hours. Our findings showed a clear CO 2 concentration decrease during COVID-19 restrictions, which is consistent with the CO 2 emissions reductions due to the pandemic. The enhancement method used in this study is an effective method to reduce the impacts of weather and background fluctuations. Low-cost sensors, which are inexpensive and convenient, could play an important role in further on-road and other urban observations. [ABSTRACT FROM AUTHOR]

To prevent the spread of the COVID-19 epidemic, restrictions such as lockdown, were conducted globally, which led to significant reduction in fossil fuel emissions, especially in urban regions. However, CO2 concentrations in urban regions are affected by many factors, such as weather and background CO2 fluctuations. Thus, it is difficult to directly observe the reductions in CO2 concentrations with sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the prohibitions, to determine the reduction signals. We conducted six on-road CO2 observations in Beijing using mobile platforms before (BC), during (DC) and after COVID-19 prohibitions (AC). To reduce the weather and background impacts, we chose trips with the most similar weather as possible and calculated the enhancement, which mean the difference in the CO2 concentration between on-road and the background level measured at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP) tower. The results showed that DC CO2 enhancement decreased by 41 parts per million (ppm) and 26 ppm compared to those during BC and AC, respectively, after eliminating the fluctuations in CO2 concentrations on polluted days. Detailed analysis showed that, during COVID, there was no difference between weekdays and weekends. The enhancements during rush hours were almost twice those during working hours, indicating that emissions during rush hours were much higher. Compared with DC and BC, the reductions in the enhancements during rush hours were much larger than those during working hours. Our findings showed a clear decrease during COVID, which are consistent with the CO2 concentration and emissions reductions due to the pandemic. The enhancement way used in this study is an effective method to reduce the impacts of weather and background fluctuation and should be regularly and more frequently conducted in future work. [ABSTRACT FROM AUTHOR]

The concept of an intelligent manufacturing system is proposed and described based on a comparison of manufacturing and biological organism systems. To promptly eliminate uncertain disturbances produced during the intelligent manufacturing system's operation, an immune monitoring model for an intelligent manufacturing system is proposed that is analogous to a biological immune system mechanism. The implementation of immune recognition, assessment, learning, memory, and control regulation in response to abnormal disturbance factors is investigated. The system's immune evaluation model based on the gray correlation analysis method is carefully analyzed. A neural network-based immune response method is used to deal with the disturbance. Finally, the paper shows how the effectiveness of the proposed method was verified experimentally through a simulation. [ABSTRACT FROM AUTHOR]

From the interdisciplinary point of manufacturing engineering, biology engineering as well as control engineering, a new type of bio-inspired manufacturing system architecture is presented based on the neuroendocrine regulation mechanism of the biological organism, and especially the general principles of hormone release control are used for adaptive production planning and control. First, a concept of organic manufacturing cell is proposed, and then a reference framework of bio-inspired manufacturing system is established based on recursive control and regulation of organic manufacturing cells. Mimicking the hormone secretion and release behavior, a multiple feedback control model of bio-inspired manufacturing system is set up, which can enable the adaptive scheduling and production control to deal with the changes and stimulus on the job shop floor. [ABSTRACT FROM AUTHOR]

Poly[bi(o-carboxyphenyl)adipate-polyethylen glycol] anhydrides-P(BOCA-PEG)- polymeric drugs were synthesized and characterized by Fourier transformed infrared spectroscopy, NMR, DSC, gel permeation chromatography etc. Salicylic acid loading efficiency of these polymers ranged from 43.5% to 71.3%, which was much higher than that of other polymeric drugs with salicylic acid. The in vitro release of salicylic acid from the polymers was carried out in buffer solutions with different pH values and rat gastroinstestinal contents. The results showed the release rate of salicylic acid increased with the increase of PEG content in the polymers and the increase of pH value of degradation buffer solution. The rat cecal contents also greatly promoted the release of salicylic acid. In 0.1M phosphate buffer solution at pH8.0, 37°C containing 5% rat cecal contents, P(BOCA-PEG200) (80:20) had 15% salicylic acid released in 21 hr, indicating its potential use in colon-specific salicylic acid delivery. [ABSTRACT FROM AUTHOR]

Background: Quantifying CO2 emissions from cities is of great importance because cities contribute more than 70% of the global total CO2 emissions. As the largest urbanized megalopolis region in northern China, the Beijing-Tianjin-Hebei (Jing-Jin-Ji, JJJ) region (population: 112.7 million) is under considerable pressure to reduce carbon emissions. Despite the several emission inventories covering the JJJ region, a comprehensive evaluation of the CO2 emissions at the prefectural city scale in JJJ is still limited, and this information is crucial to implementing mitigation strategies. Results: Here, we collected and analyzed 8 published emission inventories to assess the emissions and uncertainty at the JJJ city level. The results showed that a large discrepancy existed in the JJJ emissions among downscaled country-level emission inventories, with total emissions ranging from 657 to 1132 Mt CO2 (or 849 ± 214 for mean ± standard deviation (SD)) in 2012, while emission estimates based on provincial-level data estimated emissions to be 1038 and 1056 Mt. Compared to the mean emissions of city-data-based inventories (989 Mt), provincial-data-based inventories were 6% higher, and national-data-based inventories were 14% lower. Emissions from national-data-based inventories were 53–75% lower in the high-emitting industrial cities of Tangshan and Handan, while they were 47–160% higher in Beijing and Tianjin than those from city-data-based inventories. Spatially, the emissions pattern was consistent with the distribution of urban areas, and urban emissions in Beijing contributed 50–70% of the total emissions. Higher emissions from Beijing and Tianjin resulted in lower estimates of prefectural cities in Hebei for some national inventories. Conclusions: National-level data-based emission inventories produce large differences in JJJ prefectural city-level emission estimates. The city-level statistics data-based inventories produced more consistent estimates. The consistent spatial distribution patterns recognized by these inventories (such as high emissions in southern Beijing, central Tianjin and Tangshan) potentially indicate areas with robust emission estimates. This result could be useful in the efficient deployment of monitoring instruments, and if proven by such measurements, it will increase our confidence in inventories and provide support for policy makers trying to reduce emissions in key regions. [ABSTRACT FROM AUTHOR]

Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R2 = 0.83~0.90) and among the five sensors (R2 = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM2.5 concentrations. The relative bias reached −24.82% when the PM2.5 concentration was >250 µg/m3. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM10 concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary. [ABSTRACT FROM AUTHOR]