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 PM_2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM_2.5 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 PM_2.5 concentrations. The relative bias reached −24.82% when the PM_2.5 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.

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, NO_x, and SO₂. In addition, the residential and industrial sectors were large emitters (77–85%) of PM₁₀, PM_2.5, 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 NO_x 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, NO_x, 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., NO_x, 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 NO_x, 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.

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.

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.

China has committed to peak its carbon emissions before 2030 and achieve carbon neutrality before 2060, known as the dual carbon goals (DCGs). To support the DCG, monitoring and assessment of progresses in emissions reduction have become a central research focus. Here, we describe the Beijing–Tianjin–Hebei (JJJ) carbon monitoring system, established over the last 6 years through a collaborative effort of 16 research institutions and universities. The monitoring capability includes ground-based high-precision reference stations, a dense network of medium-accuracy low-cost sensors, carbon satellites, vertical profile samplers, and mobile platforms. Observations using the network have shown CO ₂ variations from diurnal-, synoptic- to seasonal-interannual time scales, as well as dramatic on-road changes during the COVID-19 lockdown. High-resolution modeling and data assimilation at kilometer scale reveal major deficiencies in global carbon emission datasets at regional/city scale. The inversion found the total fossil fuel CO ₂ emissions of the City of Beijing to be 117 megatonne (Mt) but much lower than the national-databased inventories (152 MtCO ₂ ). The results have been used in emission assessment by national and local environmental protection agencies. Similar systems are now being developed in other Chinese cities. Significance Statement: China’s carbon neutral goal by 2060 requires our ability to monitor and assess progresses in its path to decarbonization with unprecedented resolution at city and regional scale where most of the CO ₂ emissions occur. Since 2017, a collaborative effort from 16 research institutions has built a carbon monitoring system for the Beijing–Tianjin–Hebei (JJJ) city cluster. Data from a network of ground-, air-, and space-based observations showed CO ₂ variations at diurnal-, synoptic- to seasonal-interannual time scales, as well as dramatic on-road changes during the COVID-19 lockdown. High-resolution modeling and data assimilation at kilometer scale reveal major deficiencies in global carbon emission datasets. The results are being used in emission assessment by national and local environmental protection agencies, adopted at other Chinese cities.

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 (F_TA), based on eight precipitation products and a TBM, VEGAS. We find that the pattern of uncertainties in simulated F_TA obviously differs from the pattern of discrepancies in precipitation, owing to divergent water sensitivities of vegetation over different regions. Globally, the uncertainty in F_TA can be up to approximately 40.73% of the uncertainty in TRENDYv6 multi‐model simulated F_TA 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 F_TA with the slope of −0.0040 PgC yr⁻¹ per mm yr⁻¹ ( = 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 F_TA 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 F_TA 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.