Detailed quantification of the spatial and temporal variability of ambient fine particulate matter (PM2.5) has, to date, been limited due to the cost and logistics involved with traditional monitoring approaches. New miniaturized particle sensors are a potential strategy to gather more time- and spatially-resolved data, to address data gaps in regions with limited monitoring and to address important air quality research priorities in a more cost-effective manner. This work presents field evaluations and lab testing of three models of low-cost ( 0.80), however the same sensors had poor agreement if the comparison was restricted to lower concentrations (R2 = ~0, < 40 μg m-3). The results of this work indicate the potential usefulness of these sensors, including the PPD20V, for higher concentration applications (< ~250 μg m-3). These field- testing results provide important insights into the varying performance of low-cost PM sensors under highly contrasting atmospheric conditions. The inconsistent performance results underscore the need for rigorous evaluation of optical particle sensors in the laboratory and in diverse field environments.

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Field Test of Several Low-Cost Particulate Matter Sensors in High and Low Concentration Urban Environments.

From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors

Due to their affordability, compact size, and moderate accuracy, low-cost sensors have been studied extensively in recent years Different manufacturers employ different calibration methodologies and provide users with calibration factors for their models This study assessed the performance of nine low-cost PM monitors (AirVisual, Alphasense, APT, Awair, Dylos, Foobot, PurpleAir, Wynd, and Xiaomi) in a chamber containing a well-defined aerosol A GRIMM and a SidePak were used as the reference instruments The monitors were divided into two groups according to their working principle and data reporting format, and a linear correlation factor for the PM25 mass concentration measurement was calculated for each monitor Additionally, the differences between the mass concentrations reported by the various monitors and those measured by the reference instruments were plotted against their average before and after user calibration to demonstrate the degree of improvement possible with calibration Bin-specific calibration was also performed for monitors reporting size distributions to demonstrate coincidence errors that could bias the results Since monitors designed for residential use often display the air quality index, typically illustrating it with a simplified, color-coded index, the color schemes of various monitors were evaluated against the US EPA regulation to determine whether they could convey the overall air quality accurately and promptly Although these residential monitors indicated the air quality moderately well, their differing color schemes made the evaluation difficult and potentially inaccurate Altogether, the tested monitors offer low-cost sensors in packages that are convenient for use and ready for deployment without additional assembly However, to improve the accuracy of the measurements, user-defined calibration for the target PM source is still recommended

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Evaluation of nine low-cost-sensor-based particulate matter monitors

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

https://www.mdpi.com/1424-8220/20/23/6819/pdf

A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN–LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coefficient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance.

Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM

Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network

This paper presents CaM, a holistic cache and memory bandwidth resource allocation strategy for multicore real-time systems. CaM is designed for partitioned scheduling, where tasks are mapped onto cores, and the shared cache and memory bandwidth resources are partitioned among cores to reduce resource interferences due to concurrent accesses. Based on our extension of LITMUSRT with Intel's Cache Allocation Technology and MemGuard, we present an experimental evaluation of the relationship between the allocation of cache and memory bandwidth resources and a task's WCET. Our resource allocation strategy exploits this relationship to map tasks onto cores, and to compute the resource allocation for each core. By grouping tasks with similar characteristics (in terms of resource demands) to the same core, it enables tasks on each core to fully utilize the assigned resources. In addition, based on the tasks' execution time behaviors with respect to their assigned resources, we can determine a desirable allocation that maximizes schedulability under resource constraints. Extensive evaluations using real-world benchmarks show that CaM offers near optimal schedulability performance while being highly efficient, and that it substantially outperforms existing solutions.

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Holistic Resource Allocation for Multicore Real-Time Systems

Real-time virtualization is an emerging technology for embedded systems integration and latency-sensitive cloud applications. Earlier real-time virtualization platforms require offline configuration of the scheduling parameters of virtual machines (VMs) based on their worst-case workloads, but this static approach results in pessimistic resource allocation when the workloads in the VMs change dynamically. Here, we present Multi-Mode-Xen (M2-Xen), a real-time virtualization platform for dynamic real-time systems where VMs can operate in modes with different CPU resource requirements at run-time. M2-Xen has three salient capabilities: (1) dynamic allocation of CPU resources among VMs in response to their mode changes, (2) overload avoidance at both the VM and host levels during mode transitions, and (3) fast mode transitions between different modes. M2-Xen has been implemented within Xen 4.8 using the real-time deferrable server (RTDS) scheduler. Experimental results show that M2-Xen maintains real-time performance in different modes, avoids overload during mode changes, and performs fast mode transitions.

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Multi-Mode Virtualization for Soft Real-Time Systems

Fault-tolerant coordination services have been widely used in distributed applications in cloud environments. Recent years have witnessed the emergence of time-sensitive applications deployed in edge computing environments, which introduces both challenges and opportunities for coordination services. On one hand, coordination services must recover from failures in a timely manner. On the other hand, edge computing employs local networked platforms that can be exploited to achieve timely recovery. In this work, we first identify the limitations of the leader election and recovery protocols underlying Apache ZooKeeper, the prevailing open-source coordination service. To reduce recovery latency from leader failures, we then design RT-Zookeeper with a set of novel features including a fast-convergence election protocol, a quorum channel notification mechanism, and a distributed epoch persistence protocol. We have implemented RT-Zookeeper based on ZooKeeper version 3.5.8. Empirical evaluation shows that RT-ZooKeeper achieves 91% reduction in maximum recovery latency in comparison to ZooKeeper. Furthermore, a case study demonstrates that fast failure recovery in RT-ZooKeeper can benefit a common messaging service like Kafka in terms of message latency.

https://dl.acm.org/doi/pdf/10.1145/3477034

RT-ZooKeeper: Taming the Recovery Latency of a Coordination Service

There is increasing interest in supporting time-critical services in cloud computing environments. Those cloud services differ from traditional hard real-time systems in three aspects. First, cloud services usually involve latency requirements in terms of probabilistic tail latency instead of hard deadlines. Second, some cloud services need to handle aperiodic requests for stochastic arrival processes instead of traditional periodic or sporadic models. Finally, the computing platform must provide performance isolation between time-critical services and other workloads. It is therefore essential to provision resources to meet different tail latency requirements. As a step towards cloud services with stochastic latency guarantees, this paper presents a stochastic response time analysis for aperiodic services following a Poisson arrival process on computing platforms that schedue time-critical services as deferrable servers. The stochastic analysis enables a service operator to provision CPU resources for aperiodic services to achieve a desired tail latency. We evaluated the method in two case studies, one involving a synthetic service and another involving a Redis service, both on a testbed based on Xen 4.10. The results demonstrate the validity and efficacy of our method in a practical setting.

Haoran Li

Papers

Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network

Holistic Resource Allocation for Multicore Real-Time Systems

Multi-Mode Virtualization for Soft Real-Time Systems

RT-ZooKeeper: Taming the Recovery Latency of a Coordination Service

Predicting Latency Distributions of Aperiodic Time-Critical Services