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Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, ie, field survey, requires a significant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simplifies or over-complicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, ie, DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More specifically, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to refine the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets confirm that DeepMG significantly outperforms the state-of-the-art methods. Ministry of Education (MOE) Nanyang Technological University Accepted version The research of Cheng Long was supported by the NTU Start-Up Grant and Singapore MOE Tier 1 Grant RG20/19 (S).

The objective of public resource allocation, e.g., the deployment of billboards, surveillance cameras, base stations, trash bins, is to serve more people. However, due to the dynamics of human mobility patterns, people are distributed unevenly on the spatial and temporal domains. As a result, in many cases, redundant resources have to be deployed to meet the crowd coverage requirements, which leads to high deployment costs and low usage. Fortunately, with the development of unmanned vehicles, the dynamic allocation of those public resources becomes possible. To this end, we provide the first attempt to design an effective and efficient scheduling algorithm for the dynamic public resource allocation. We formulate the problem as a novel multi-agent long-term maximal coverage scheduling (MALMCS) problem, which considers the crowd coverage and the energy limitation during a whole day. Two main components are employed in the system: 1) multi-step crowd flow prediction, which makes multi-step crowd flow prediction given the current crowd flows and external factors; and 2) energy adaptive scheduling, which employs a two-step heuristic algorithm, i.e., energy adaptive scheduling (EADS), to generate a scheduling plan that maximizes the crowd coverage within the service time for agents. Extensive experiments based on real crowd flow data in Happy Valley (a popular theme park in Beijing) demonstrate the effectiveness and efficiency of our approach.

People often refer to a place of interest (POI) by an alias. In e-commerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.

With the completion of the Beijing Five-year Clean Air Action Plan by the end of 2017, the annual mean PM2.5 concentration in Beijing dropped dramatically to 58.0 µg m−3 in 2017 from 89.5 µg m−3 in 2013. However, controversies exist to argue that favourable meteorological conditions in 2017 were the major driver for such a rapid decrease in PM2.5 concentrations. To comprehensively evaluate this 5-year plan, we employed a Kolmogorov–Zurbenko (KZ) filter and WRF-CMAQ (Weather Research and Forecasting and the Community Multi-scale Air Quality) to quantify the relative contribution of meteorological conditions and the control of anthropogenic emissions to PM2.5 reduction in Beijing from 2013 to 2017. For these 5 years, the relative contribution of emission reduction to the decrease in PM2.5 concentrations calculated by KZ filtering and WRF-CMAQ was 80.6 % and 78.6 % respectively. KZ filtering suggested that short-term variations in meteorological and emission conditions contributed majorly to rapid changes in PM2.5 concentrations in Beijing. WRF-CMAQ revealed that the relative contribution of local and regional emission reduction to the PM2.5 decrease in Beijing was 53.7 % and 24.9 % respectively. For local emission-reduction measures, the regulation of coal boilers, increasing use of clean fuels for residential use and industrial restructuring contributed 20.1 %, 17.4 % and 10.8 % to PM2.5 reduction respectively. Both models suggested that the control of anthropogenic emissions accounted for around 80 % of the PM2.5 reduction in Beijing, indicating that emission reduction was crucial for air quality enhancement in Beijing from 2013 to 2017. Consequently, such a long-term air quality clean plan should be continued in the following years to further reduce PM2.5 concentrations in Beijing.

Due to complicated ozone formation regimes controlled by both ozone precursors and meteorological conditions, the underlying drivers for soaring ozone concentrations in Beijing were limitedly examined. With ground observation and remotely sensed data, the variation of ground ozone concentrations at an urban AT (Aoti) station and a rural YF (Yufa) station in Beijing was examined and a notable upward trend of ozone concentrations at both stations was found from 2006 to 2017. Following this, through a statistical model, the relative contribution of meteorological conditions to long-term ozone variations at both stations was calculated as 2%–3%, indicating anthropogenic emissions were the major cause for long-term ozone variations in Beijing. Meanwhile, short-term ozone variations could be influenced significantly by seasonal and synoptic meteorological conditions. Furthermore, the long-term variations of ozone formation regimes across Beijing were analyzed using OMI (Ozone Monitoring Instrument) retrieved HCHO VCDs (Vertical Column Densities)/NO2 VCDs, which were strongly correlated with ozone concentrations. The results suggested that the ozone formation regime for the AT urban station and YF rural station had changed from VOCs-limited and NOx-limited to VOCs-NOx-limited respectively. In this case, NOx-oriented emission-reduction measures for reducing PM2.5 concentrations might conversely enhance ozone concentrations in Beijing. Given the increasingly heterogeneous distribution of different ozone formation regimes across Beijing, emission-reduction strategies that balanced consider the reduction of VOCs and NOx emissions should be better designed and implemented according to local ozone formation regimes.

To comprehensively evaluate the effects of the recent “2+26” regional strategy for air quality improvement, we compared the variations in PM2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM2.5 concentration 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM2.5 concentration 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly, and nitrate ions were the dominant PM2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contribution of coal combustion to PM2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 %, respectively, indicating that the recent “Coal to Gas” project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing–Tianjin–Hebei region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 %, respectively. The relative contribution of local emissions to PM2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intentionally reduce the dominant contribution of vehicle exhausts to PM2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework.

Summary Brassica napus (AACC, 2n = 38) is an important oilseed crop grown worldwide. However, little is known about the population evolution of this species, the genomic difference between its major genetic groups, such as European and Asian rapeseed, and the impacts of historical large‐scale introgression events on this young tetraploid. In this study, we reported the de novo assembly of the genome sequences of an Asian rapeseed (B. napus), Ningyou 7, and its four progenitors and compared these genomes with other available genomic data from diverse European and Asian cultivars. Our results showed that Asian rapeseed originally derived from European rapeseed but subsequently significantly diverged, with rapid genome differentiation after hybridization and intensive local selective breeding. The first historical introgression of B. rapa dramatically broadened the allelic pool but decreased the deleterious variations of Asian rapeseed. The second historical introgression of the double‐low traits of European rapeseed (canola) has reshaped Asian rapeseed into two groups (double‐low and double‐high), accompanied by an increase in genetic load in the double‐low group. This study demonstrates distinctive genomic footprints and deleterious SNP (single nucleotide polymorphism) variants for local adaptation by recent intra‐ and interspecies introgression events and provides novel insights for understanding the rapid genome evolution of a young allopolyploid crop.

Recently, ground ozone has become one major airborne pollutant and the frequency of ozone-induced pollution episodes has increased rapidly across China. However, due to the lack of long-term observation data, relevant research on the characteristics and influencing factors of urban ozone concentrations remains limited. Based on ground ozone observation data during 2006-2016, we quantified the causality influence of individual meteorological factors on ozone concentrations in Beijing using a convergent cross mapping (CCM) method. The result indicated that the influence of each meteorological factor on ozone concentrations varied significantly across seasons and years. At the inter-annual scale, all-year meteorological influences on ozone concentrations were much more stable than seasonal meteorological influences. At the seasonal scale, meteorological influences on ozone concentrations were stronger in spring and autumn. Amongst multiple individual factors, temperature was the key meteorological influencing factor for ozone concentrations in all seasons except winter, when wind, humidity and SSD exerted major influences on ozone concentrations. In addition to temperature, air pressure was another meteorological factor that exerted strong influences on ozone concentrations. At both the inter-annual and seasonal scale, the influence of temperature and humidity on ozone concentrations was generally stable whilst that of other factors experienced large variations. Different from PM2.5, meteorological influences on ozone concentrations were relatively weak in summer, when ozone concentrations were the highest in Beijing. Given the generally stable meteorological influences on ozone concentrations and human-induced emissions of VOCs and NOx across seasons, warming induced notable increase in summertime biogenic emissions of VOCs and NOx can be a major driver for the increasing ozone pollution episodes. This research provides useful references for understanding long-term meteorological influences on ozone concentrations in mega cities in China.

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Additional file of Construction of an integrated genetic linkage map for the A genome of Brassica napus using SSR markers derived from sequenced BACs in B. rapa

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The vigor of tea plants (Camellia sinensis) and tea quality are strongly influenced by the abundance and forms of nitrogen, principally NO3&minus, NH4+, and amino acids. Mechanisms to access different nitrogen sources and the regulatory cues remain largely elusive in tea plants. A transcriptome analysis was performed to categorize differentially expressed genes (DEGs) in roots and young leaves during the early response to four nitrogen treatments. Relative to the continuously nitrogen-replete control, the three nitrogen-deprived and resupplied treatments shared 237 DEGs in the shoots and 21 DEGs in the root. Gene-ontology characterization revealed that transcripts encoding genes predicted to participate in nitrogen uptake, assimilation, and translocation were among the most differentially expressed after exposure to the different nitrogen regimes. Because of its high transcript level regardless of nitrogen condition, a putative amino acid transporter, TEA020444/CsCAT9.1, was further characterized in Arabidopsis and found to mediate the acquisition of a broad spectrum of amino acids, suggesting a role in amino acid uptake, transport, and deposition in sinks as an internal reservoir. Our results enhance our understanding of nitrogen-regulated transcript level patterns in tea plants and pinpoint candidate genes that function in nitrogen transport and metabolism, allowing tea plants to adjust to variable nitrogen environments.

The rapid development of e-commerce requires efficient and reliable logistics services. Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the accurate delivery time of each parcel manually, which provides vital information for applications like delivery insurances, delivery performance evaluations, and customer available time discovery. Couriers' trajectories generated by their PDAs provide a chance to infer the delivery time automatically to ease the burdens on the couriers. However, directly using the nearest stay point to infer the delivery time is under satisfactory due to two challenges: 1) inaccurate delivery locations, and 2) various stay scenarios. To this end, we propose Delivery Time Inference (DTInf), to automatically infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which detects stay points from trajectories, and separates stay points and waybills by delivery trips, 2) Delivery Location Correction, which infers true delivery locations of waybills by mining historical deliveries, and 3) Delivery Event-based Matching, which selects the best-matched stay point for waybills in the same delivery location to infer the delivery time. Extensive experiments and case studies based on large scale real-world waybill and trajectory data from JD Logistics confirm the effectiveness of our approach. Finally, we introduce a system based on DTInf, which is deployed and used internally in JD Logistics. Ministry of Education (MOE) Nanyang Technological University Accepted version This work was also supported by the Nanyang Technological University Start-UP Grant from the College of Engineering under Grant M4082302 and by the Ministry of Education, Singapore, under its Academic Research Fund Tier 1 (RG20/19 (S)).

Air pollution over China has attracted wide interest from public and academic community. PM2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM2.5 concentrations are essential to understand the variability of PM2.5 and seek methods to control PM2.5. Since 2013, the measurement of PM2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM2.5 concentrations. We start with an introduction of general meteorological conditions and PM2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM2.5 concentrations. Next, major methods used to quantify meteorological influences on PM2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM2.5. The feedback effects of PM2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM2.5 pollution are made finally.

With the development of positioning technology, a large number of trajectories have been generated, which are very useful for many urban applications. However, it is challenging to manage trajectory data for its spatio-temporal dynamics and high-volume properties. Existing trajectory data management frameworks suffer from efficiency or scalability problem, and only support limited trajectory query types. This paper takes the first attempt to build a holistic distributed NoSQL trajectory storage engine, named TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa can manage a prohibitively large number of trajectories, and support plenty of query types efficiently. Specifically, we first design a novel trajectory storage schema, which reduces the storage size tremendously. We then devise a novel indexing key schema for time ranges, based on which ID temporal query can be supported efficiently. To reduce the amount of retrieved trajectory data for a spatial range query, we innovatively propose a position code to indicate the spatial location of trajectories accurately. We also propose a bunch of pruning strategies for similarity query and k-NN query in the NoSQL environment. Extensive experiments are conducted using two real datasets and one synthetic dataset, verifying the powerful query efficiency and scalability of TrajMesa.

Ground level ozone exerts a strong impact on crop yields, yet how to properly quantify ozone induced yield losses in China remains challenging. To this end, we employed a series of O3-crop models to estimate ozone induced yield losses in China from 2014 to 2018. The outputs from all models suggested that the total Relative Yield Losses (RYL) of wheat in China from 2014 to 2018 was 18.4%-49.3% and the total RYL of rice was 6.2%-52.9%. Consequently, the total Crop Production Losses (CPL) of wheat and rice could reach 63.9-130.4 and 28.3-35.4 million tons, and the corresponding Total Economic Losses (TEL) could reach 20.5-44.7 and 11.0-15.3 billion dollars, stressing the great importance and urgency of national ozone management. Meanwhile, the estimation outputs highlighted the large variations between different regional O3-crop models when applying to large scales. Instead of applying one unified O3-crop models to all regions across China, we also explored the strategy of employing specific O3-crop models in corresponding (and neighboring) regions to estimate ozone induced yield loss in China. The comparison of two strategies suggested that the mean value from multiple models may still present an inconsistent over/underestimation trend for different crops. Therefore, it is a preferable strategy to employ corresponding O3-crop models in different regions for estimating the national crop losses caused by ozone pollution. However, the severe lack of regional O3-crop models in most regions across China makes a robust estimation of national yield losses highly challenging. Given the large variations between O3-crop interactions across regions, a systematic framework with massive regional O3-crop models should be properly designed and implemented.

Air pollution over China has attracted wide interest from public and academic community. PM

A high-density single nucleotide polymorphism (SNP) Illumina Infinium array, containing 52,157 markers, was developed for the allotetraploid Brassica napus. A stringent selection process employing the short probe sequence for each SNP assay was used to limit the majority of the selected markers to those represented a minimum number of times across the highly replicated genome. As a result approximately 60 % of the SNP assays display genome-specificity, resolving as three clearly separated clusters (AA, AB, and BB) when tested with a diverse range of B. napus material. This genome specificity was supported by the analysis of the diploid ancestors of B. napus, whereby 26,504 and 29,720 markers were scorable in B. oleracea and B. rapa, respectively. Forty-four percent of the assayed loci on the array were genetically mapped in a single doubled-haploid B. napus population allowing alignment of their physical and genetic coordinates. Although strong conservation of the two positions was shown, at least 3 % of the loci were genetically mapped to a homoeologous position compared to their presumed physical position in the respective genome, underlying the importance of genetic corroboration of locus identity. In addition, the alignments identified multiple rearrangements between the diploid and tetraploid Brassica genomes. Although mostly attributed to genome assembly errors, some are likely evidence of rearrangements that occurred since the hybridisation of the progenitor genomes in the B. napus nucleus. Based on estimates for linkage disequilibrium decay, the array is a valuable tool for genetic fine mapping and genome-wide association studies in B. napus and its progenitor genomes., yes

To comprehensively evaluate the effects of the recent 2+26 regional strategy for air quality improvement, we compared the variations in PM2.5 concentrations in Beijing during four pollution episodes with different emission-reduction strategies. The 2+26 strategy implemented in March 2018 led to a mean PM2.5 concentrations of 16.43 % lower than that during the pollution episode in March 2013, when no specific emission-reduction measures were in place. The same 2+26 strategy implemented in November 2017 led to a mean PM2.5 concentrations of 32.70 % lower than that during the pollution episode in November 2016, when local emission-reduction measures were implemented. The results suggested that the effects of the 2+26 regional emission-reduction measures on PM2.5 reductions were influenced by a diversity of factors and could differ significantly during specific pollution episodes. Furthermore, we found the proportions of sulfate ions decreased significantly and nitrate ions were the dominant PM2.5 components during the two 2+26 orange alert periods. Meanwhile, the relative contributions of coal combustion to PM2.5 concentrations in Beijing during the pollution episodes in March 2013, November 2016, November 2017 and March 2018 was 40 %, 34 %, 28 % and 11 % respectively, indicating that the recent Coal to Gas project and the contingent 2+26 strategy led to a dramatic decrease in coal combustion in the Beijing-Tianjin-Hebei Region. On the other hand, the relative contribution of vehicle exhaust during the 2+26 orange alert periods in November 2017 and March 2018 reached 40 % and 54 % respectively. The relative contribution of local emission to PM2.5 concentrations in Beijing also varied significantly and ranged from 49.46 % to 89.35 % during the four pollution episodes. These results suggested that the 2+26 regional emission-reduction strategy should be implemented with red air pollution alerts during heavy pollution episodes to intendedly reduce the dominant contribution of vehicle exhausts to PM2.5 concentrations in Beijing, while specific emission-reduction measures should be implemented accordingly for different cities within the 2+26 framework.

Biological network alignment aims to discover important similarities and differences and thus find a mapping between topological and/or functional components of different biological molecular networks. Then, the mapped components can be considered to correspond to both their places in the network topology and their biological attributes. Development and evolution of biological network alignment methods has been accelerated by the rapidly increasing availability of such biological networks, yielding a repertoire of tens of methods based upon graph theory. However, most biological processes, especially the metabolic reactions, are more sophisticated than simple pairwise interactions and contain three or more participating components. Such multi-lateral relations are not captured by graphs, and computational methods to overcome this limitation are currently lacking. This paper introduces hypergraphs and association hypergraphs to describe metabolic networks and their potential alignments, respectively. Within this framework, metabolic networks are aligned by identifying the maximal Z-eigenvalue of a symmetric tensor. A shifted higher-order power method was utilized to identify a solution. A rotational strategy has been introduced to accelerate the tensor-vector product by 250-fold on average and reduce the storage cost by up to 1,000-fold. The algorithm was implemented on a spark-based distributed computation cluster to significantly increase the convergence rate further by 50- to 80-fold. The parameters have been explored to understand their impact on alignment accuracy and speed. In particular, the influence of initial value selection on the stationary point has been simulated to ensure an accurate approximation of the global optimum. This framework was demonstrated by alignments among the genome-wide metabolic networks of Escherichia coli MG-1655 and Halophilic archaeon DL31. To our knowledge, this is the first genome-wide metabolic network alignment at both the metabolite level and the enzyme level. These results demonstrate that it can supply quite a few valuable insights into metabolic networks. First, this method can access the driving force of organic reactions through the chemical evolution of metabolic network. Second, this method can incorporate the chemical information of enzymes and structural changes of compounds to offer new way defining reaction class and module, such as those in KEGG. Third, as a vertex-focused treatment, this method can supply novel structural and functional annotation for ill-defined molecules. The related source code is available on request.

SummaryBrassica napus (AACC, 2n=38), is an important oilseed crop grown worldwide. However, little is known about the population evolution of this species, the genomic difference between its major genetic clusters, such as European and Asian rapeseed, and impacts of historical large-sale introgression events in this young tetraploid. In this study, we reported the de novo assembly of the genome sequences of an Asian rapeseed (B. napus), Ningyou 7 and its four progenitors and carried out de novo assembly-based comparison, pedigree and population analysis with other available genomic data from diverse European and Asian cultivars. Our results showed that Asian rapeseed originally derived from European rapeseed, but it had subsequently significantly diverged, with rapid genome differentiation after intensive local breeding selection. The first historical introgression of B. rapa dramatically broadened the allelic pool of Asian B. napus, but decreased their deleterious variations. The secondary historical introgression of European rapeseed (canola-quality) has reshaped Asian rapeseed into two groups, accompanied by an increase in genetic load. This study demonstrates distinctive genomic footprints by recent intra- and inter-species introgression events for local adaptation, and provide novel insights for understanding the rapid genome evolution of a young allopolyploid crop.

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect illegal vehicle parking events rely highly on active human efforts, e.g., police patrols or surveillance cameras. However, these approaches are extremely ineffective to cover a large city. The massive and high quality sharing bike trajectories from Mobike offer us with a unique opportunity to design a ubiquitous illegal parking detection system, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. Two main components are employed to mine the trajectories in our system: 1)~trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds indexes for bike trajectories; and 2)~illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events. The system is deployed on the cloud internally used by Mobike. Finally, extensive experiments and many insightful case studies based on the massive trajectories in Beijing are presented.

In recent years, particulate matter (PM) pollution has increasingly affected public life and health. Therefore, crop residue burning, as a significant source of PM pollution in China, should be effectively controlled. This study attempts to understand variations and characteristics of PM10 and PM2.5 concentrations and discuss correlations between the variation of PM concentrations and crop residue burning using ground observation and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The results revealed that the overall PM concentration in China from 2013 to 2017 was in a downward tendency with regional variations. Correlation analysis demonstrated that the PM10 concentration was more closely related to crop residue burning than the PM2.5 concentration. From a spatial perspective, the strongest correlation between PM concentration and crop residue burning existed in Northeast China (NEC). From a temporal perspective, the strongest correlation usually appeared in autumn for most regions. The total amount of crop residue burning spots in autumn was relatively large, and NEC was the region with the most intense crop residue burning in China. We compared the correlation between PM concentrations and crop residue burning at inter-annual and seasonal scales, and during burning-concentrated periods. We found that correlations between PM concentrations and crop residue burning increased significantly with the narrowing temporal scales and was the strongest during burning-concentrated periods, indicating that intense crop residue burning leads to instant deterioration of PM concentrations. The methodology and findings from this study provide meaningful reference for better understanding the influence of crop residue burning on PM pollution across China.

Crop residue burning, which is a convenient approach to process excessive crop straws, has a negative impact on local and regional air quality and soil structures. China, as a major agricultural country with a large population, should take more effective measures to control crop residue burning. In this case, a better understanding of long-term spatio-temporal variations of crop residue burning in China is required. The MODIS products MOD14A1/MYD14A1 were employed in this research. Meanwhile, due to the vast territory of China, we divided the study area into seven regions based on the national administrative divisions to examine crop residue burning in each region, respectively. The temporal analysis of crop residue burning in different regions demonstrates a fluctuated, but generally upward, trend from 2003 to 2017. For monthly variations of crop residue burning in different regions, detected fire spots in June mainly concentrated in Central China (CC), East China (EC), and North China (NC). A majority of detected fire spots in Northeast China (NEC) and Northwest China (NWC) appeared in April and October. For other months, a small number of fire spots were distributed in all regions in a scattered manner. Furthermore, from a spatio-temporal perspective, this research revealed that crop residue burning in NEC was the most active among all regions both in spring and autumn. For summer, EC holds a larger proportion of burning spots than other regions. For winter, the number of burning spots in most regions was close. This research conducts a comprehensive analysis of crop residue burning in China at both a national and regional scale. The methodology and results from this research provide useful reference for better monitoring and controlling crop residue burning in China.