Miniature Air Research Articles

Design and Development of an Air–Land Amphibious Inspection Drone for Fusion Reactor

This paper proposes a design method for a miniature air–land amphibious inspection drone (AAID) to be used in the latest compact fusion reactor discharge gap observation mission. Utilizing the amphibious function, the AAID realizes the function of crawling transportation in the narrow maintenance channel and flying observation inside the fusion reactor. To realize miniaturization, the mobile platform adopts the bionic cockroach wheel-legged system to improve the obstacle-crossing ability. The flight platform adopts an integrated rotor structure with frame and control to reduce the overall weight of the AAID. Based on the AAID dynamic model and the optimal control method, the control strategies under flight mode, hover mode and fly–crawl transition are designed, respectively. Finally, the prototype of the AAID is established, and the crawling, hovering, and fly–crawling transition control experiments are carried out, respectively. The test results show that the maximum crawling inclination of the AAID is more than 20°. The roll angle, pitch angle, and yaw angle deviation of the AAID during hovering are all less than 2°. The landing success rate of the AAID during the fly–crawl transition phase also exceeded 77%, proving the effectiveness of the structural design and dynamic control strategy.

Advancing sustainable air quality through calibration of miniature air quality monitors with SRA-SVR combined model

Effective calibration of miniature air quality monitor measurements is an important task to ensure accurate measurements and guarantee sustainable air quality. The aim of this study is to calibrate the measurement data of miniature air quality monitors using Stepwise Regression Analysis and Support Vector Regression (SRA-SVR) combined model. Firstly, a stepwise regression analysis model is used to find a linear relationship between the measured data from the miniature air quality monitor and the air pollutant concentration. Secondly, support vector regression is used to extract the non-linear relationships which affect the pollutant concentrations hidden in the residuals of the stepwise regression analysis model. Finally, the residual calibration values of the SVR model outputs are added to the SRA model outputs to obtain the final outputs of the SRA-SVR combined model for the pollutants. Mean absolute error, relative mean absolute percent error and root mean square error are used to compare the effectiveness of the SRA-SVR combined model and some other commonly used statistical models for the calibration of miniature air quality monitors. The results show that the SRA-SVR combination model performs optimally on both the training and test sets, regardless of which pollutant and which indicator. The SRA-SVR combined model not only has the advantages of the SRA model’s strong interpretability and the SVR model’s high accuracy, but also has higher accuracy than the single model. By using this model to calibrate the measurements of the miniature air quality monitor, its accuracy can be improved by 61.33%–87.43%.

Impact of landfill characteristics on the atmospheric exposure to halogenated flame retardants in gulls

Large amounts of consumer products containing halogenated flame retardants (HFRs) are disposed of annually in landfills, which may lead to significant releases of these semi-volatile contaminants into the environment. During their foraging activities in landfills, gulls can be exposed to elevated levels of HFRs in air. Ring-billed gulls (Larus delawarensis) breeding in the densely populated Montreal area (QC, Canada) are significantly exposed to air levels of polybrominated diphenyl ethers (PBDEs) in or in the vicinity of landfills. However, no information is currently available on the specific characteristics of these landfills that can modulate the atmospheric exposure of ring-billed gulls to HFRs. The objective of this study was to investigate how atmospheric exposure in ring-billed gulls to PBDEs and other HFRs is influenced by selected landfill characteristics (i.e., daily cover materials, waste types and tonnage). Miniature passive air samplers (PASs) combined with GPS dataloggers were deployed for ten days during six years on the back of wild-caught ring-billed gulls breeding in the Montreal area. Atmospheric levels of several PBDEs and other HFRs determined in PASs were found to increase with the presence probability of gulls in the two largest landfills using automotive shredder residues as daily cover material. Weather variables including relative humidity and wind speed had a weak influence on atmospheric levels of HFRs in the bird-borne PASs. Our results suggest that automotive shredder residues represent a significant emission source of HFRs into the air of landfills, thus influencing atmospheric exposure of gulls and other birds foraging in these sites.

A Compact Fabry–Pérot Acoustic Sensor Based on Silicon Optical Waveguide Bragg Gratings

No membranous optical sensors have excellent development prospects in aerospace and other industrial fields due to their small size and anti-electromagnetic interference. Here, we proposed a novel Fabry–Pérot (FP) cavity acoustic sensor based on silicon optical waveguide Bragg gratings. The FP cavity consists of two Bragg gratings written on the silicon-based optical waveguide and a miniature air groove. When the sound signal acts on the miniature air groove, the sound pressure changes the density of air molecules near the waveguide grating’s evanescent field, causing variation in the air’s refractive index. This results in a shift in the reflection spectrum of the FP cavity to detect the sound signal. The effects of the grating period, grating pitch quantity, and groove depth of the FP cavity on acoustic sensing were studied. The modelling predicts that the sensing sensitivity could be 0.4 nm/Pa. Theoretically, the compact self-designed acoustic sensor can withstand temperatures above 800 °C. Therefore, it has significant potential applications in precision measurement in high-temperature and high-pressure environments.

COMPARATIVE ANALYSIS OF AIRFOIL SELECTION FOR MINIATURE AIR VEHICLE

This article outlines factors to take into account when choosing an MAV airfoil (Miniature Air Vehicle). A MAV is mostly used in hazardous and defense regions that are inaccessible to humans. The MAV has some specified range of speed in which it has travel, i.e.: - 9 to 20m/s. Here, a few airfoils are chosen, and after doing a comparison analysis, the airfoil that produces the best results is chosen. The airfoil is chosen with a flat lower surface for comparative examination. In order to choose the right airfoil, "ANSYS FLUENT" is used to analyze the airfoil and calculate the lift to drag ratio. The reason for this criterion is that the MAV should have a stable flight. This article provides guidance on choosing the ideal airfoil for gliding. The airfoil with the maximum lift to drag ratio is being selected. According to the study of the data, NACA-2205 would be the ideal for MAV since it has a high lift to drag ratio at a high angle of attack and a high capability of gliding.

Active cooling of twisted coiled actuators via fabric air channels.

Twisted coiled actuators (TCAs) are promising artificial muscles for wearable soft robotic devices due to their biomimetic properties, inherent compliance, and slim profile. These artificial muscles are created by super-coiling nylon thread and are thermally actuated. Unfortunately, their slow natural cooling rate limits their feasibility when used in wearable devices for upper limb rehabilitation. Thus, a novel cooling apparatus for TCAs was specifically designed for implementation in soft robotic devices. The cooling apparatus consists of a flexible fabric channel made from nylon pack cloth. The fabric channel is lightweight and could be sewn onto other garments for assembly into a soft robotic device. The TCA is placed in the channel, and a miniature air pump is used to blow air through it to enable active cooling. The impact of channel size on TCA performance was assessed by testing nine fabric channel sizes-combinations of three widths (6, 8, and 10 mm) and three heights (4, 6, and 8 mm). Overall, the performance of the TCA improved as the channel dimensions increased, with the combination of a 10 mm width and an 8 mm height resulting in the best balance between cooling time, heating time, and stroke. This channel was utilized in a follow-up experiment to determine the impact of the cooling apparatus on TCA performance. In comparison to passive cooling without a channel, the channel and miniature air pump reduced the TCA cooling time by 42% ( s to s, ). Unfortunately, there was also a 9% increase in the heating time ( s to s, ) and a 28% decrease in the stroke ( mm to mm, ). This work demonstrates that fabric cooling channels are a viable option for cooling thermally actuated artificial muscles within a soft wearable device. Future work can continue to improve the channel design by experimenting with other configurations and materials.

Halogenated flame retardant exposure pathways in urban-adapted gulls: Are atmospheric routes underestimated?

Urban-adapted gulls can be exposed to flame retardants while foraging in landfills where elevated concentrations of polybrominated diphenyl ethers (PBDEs) and other halogenated flame retardants (HFRs) have frequently been measured in air. However, the contribution of atmospheric exposure has largely been overlooked compared to dietary exposure in birds and other wildlife. The overall objective of this study was to investigate the contribution of atmospheric exposure pathways relative to diet for PBDEs and other HFRs in ring-billed gulls (Larus delawarensis) nesting in the densely populated Montreal area (QC, Canada). Miniature passive air samplers (PASs) were deployed on the back of wild-caught ring-billed gulls for ten days. Concentrations of PBDEs and other HFRs were determined in PASs carried by ring-billed gulls as well as their lungs, stomach content, liver, preen oil, and onto the surface of their feathers. We evaluated the atmospheric and dietary exposure routes for the most abundant HFRs in samples using a structural equation model implemented in a Bayesian framework. Results indicated that lung concentrations of BDE-28 increased with its levels in air determined using bird-borne PASs. No association was found between BDE-28 concentrations in lungs and liver, whereas BDE-209 concentrations in liver increased with those in lungs. Moreover, BDE-28 and -47 concentrations in liver increased with those on feather surface, while liver BDE-47 concentrations were also positively related with those in stomach content. These findings suggested that, in addition to dietary exposure, atmospheric exposure pathways through inhalation and co-ingestion during feather maintenance (preening) significantly contribute to the accumulation of PBDEs in liver of ring-billed gulls. Atmospheric exposure to HFRs should therefore be considered in future landfill-foraging wildlife species as a potential exposure route compared to the traditional dietary exposure pathway.

One-year spatiotemporal variations of air pollutants in a major chemical-industry park in the Yangtze River Delta, China by 30 miniature air quality monitoring stations

Fine chemical industrial park (FCIP) is a major source of atmospheric pollutants in China. A long-term high spatial resolution monitoring campaign on air pollutants had been firstly conducted in a major FCIP in Yangtze River Delta (YRD) from December 2019 to November 2020. The grid-based monitoring platform consisting of 30 miniature air quality monitoring stations (AQMSs) provided comprehensive coverage of a FCIP, and long-term monitoring studies solved the problem of lack of clarity about pollution sources in industrial parks. Overall, NO2 pollution was particularly high in the pharmaceutical industry, while TVOCs and O3 pollution were most serious in the textile dyeing industry, with PM pollution much higher in the metal smelting industry than in other industries, and in the leather industry, O3 pollution was relatively severe. The spatial and temporal variations of air pollutants showed that higher PM, CO and NO2 concentrations were revealed in winter while lower in summer due to better meteorological diffusion conditions. TVOCs concentrations were higher with an average of 1954 ppb in summer possibly due to their increased volatilization from their sources at higher ambient temperatures. O3 concentrations were at their peaks in spring (88.8 μg m−3) and early fall (78.5 μg m−3). The daily trends of O3 precursors (TVOCs and NO2) were clearly negatively correlated with O3, and they showed bimodal peaks due to anthropogenic activities, plant emissions, lowering of the mixed boundary layer, etc. The O3 formed in FCIP was judged to be NO2-limited during the monitoring period based on the ratios of NO2 to TVOCs. Therefore, the effective strategy to reduce O3 formation in FCIP is to decrease the ambient NO2 concentration. Based on Pearson correlation coefficients, it appeared that WS promoted O3 formation through long-term transport and that high air temperatures also contributed to O3 formation in the environment. It was also stated in the study that the closer the residential area is to the industrial sources, the more significant the correlation. Thus, the results of this study will also be helpful for policymakers to design pollutant control strategies for different industries to mitigate the impact of pollutants on human health.

Portable and miniature mercury analyzer using direct sampling inbuilt-metal ceramic electrothermal vaporization

In this work, a small-size inbuilt-metal ceramic heater (IMCH) was for the first time utilized as a solid sampling electrothermal vaporizer (ETV), and then a novel direct sampling mercury analyzer coupled with a miniature atomic absorption spectrometer was thereby fabricated for sensitive determination of mercury in soil. The mercury analyzer is mainly composed of an IMCH-ETV, a catalytic pyrolysis furnace (CPF) using Al2O3 particles as a new filler, an atomic absorption spectrometer (AAS) and a miniature air pump for carrier gas; herein, the mostly used gold amalgamation was canceled. The IMCH with 30 W heating fulfills the 100% vaporization of mercury from up to 80 mg soil samples using 0.1 L min−1 air carrier. Under the optimized conditions, the method detection limit (LOD) was 0.4 ng g−1 for a 50 mg sample and the RSD of 11 repeated measurements of GSS-3a soil certified reference material (CRM) was 4%. Furthermore, the measured Hg values in various soil CRMs and real soil samples were within their certified values and consistent with that using the Chinese standard method (solid sampling catalytic pyrolysis AAS with gold amalgamation); and the recoveries were 85–113%, which proved favorable analytical accuracy and precision. This fabricated instrumentation only occupies 5 kg and 200 W power consumption vs. more than 25 kg and 1000 W for the traditional Hg analyzer. Therefore, the proposed IMCH-ETV-AAS method is very suitable for portable and rapid detection of mercury in soil.

Learning Pneumatic Non-Prehensile Manipulation With a Mobile Blower

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerodynamic forces, a blowing controller must (i) continually adapt to unexpected changes from its actions, (ii) maintain fine-grained control, since the slightest misstep can result in large unintended consequences (e.g., scatter objects already in a pile), and (iii) infer long-range plans (e.g., move the robot to strategic blowing locations). We tackle these challenges in the context of deep reinforcement learning, introducing a multi-frequency version of the spatial action maps framework. This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control for dynamic mobile manipulation. Experiments show that our system learns efficient behaviors for the task, demonstrating in particular that blowing achieves better downstream performance than pushing, and that our policies improve performance over baselines. Moreover, we show that our system naturally encourages emergent specialization between the different subpolicies spanning low-level fine-grained control and high-level planning. On a real mobile robot equipped with a miniature air blower, we show that our simulation-trained policies transfer well to a real environment and can generalize to novel objects.

A miniaturized 3D printed pressure regulator (µPR) for microfluidic cell culture applications

Well-defined fluid flows are the hallmark feature of microfluidic culture systems and enable precise control over biophysical and biochemical cues at the cellular scale. Microfluidic flow control is generally achieved using displacement-based (e.g., syringe or peristaltic pumps) or pressure-controlled techniques that provide numerous perfusion options, including constant, ramped, and pulsed flows. However, it can be challenging to integrate these large form-factor devices and accompanying peripherals into incubators or other confined environments. In addition, microfluidic culture studies are primarily carried out under constant perfusion conditions and more complex flow capabilities are often unused. Thus, there is a need for a simplified flow control platform that provides standard perfusion capabilities and can be easily integrated into incubated environments. To this end, we introduce a tunable, 3D printed micro pressure regulator (µPR) and show that it can provide robust flow control capabilities when combined with a battery-powered miniature air pump to support microfluidic applications. We detail the design and fabrication of the µPR and: (i) demonstrate a tunable outlet pressure range relevant for microfluidic applications (1–10 kPa), (ii) highlight dynamic control capabilities in a microfluidic network, (iii) and maintain human umbilical vein endothelial cells (HUVECs) in a multi-compartment culture device under continuous perfusion conditions. We anticipate that our 3D printed fabrication approach and open-access designs will enable customized µPRs that can support a broad range of microfluidic applications.

Calibration of miniature air quality detector monitoring data with PCA–RVM–NAR combination model

The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA–RVM–NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA–RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA–RVM–NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA–RVM–NAR model and other commonly used models such as multiple linear regression model, support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA–RVM–NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8–93.9%.

A Review of Bat-Inspired Shape Morphing Robotic Design

AbstractBy virtue of distinguished wing shape morphing characteristics, the unrivaled agility and flight maneuverability of bats have inspired scientists and engineers to develop novel forms of robots that can fly like bats. The unique wing conformations, flight kinematics, and aerodynamics offer significant advantages over the conventional form of miniature air vehicle in terms of quiet, safe operations, improved efficiency, and enhanced maneuverability. Meanwhile, they also pose substantial challenges for robot design from multiple perspectives, including mechanical design, sensing, control, etc. The practical benefits and technical bottleneck have motivated the development of bat-inspired robots in recent years. The purpose of this paper is to summarize the designing principles and report current state-of-the-art of bat-inspired robot designs, emphasizing the respective distinguishing features of each paradigm, along with the room for further improvement. Rather than showcasing advancement in wing materials, we will focus on the mechanical design and control methodology. This paper will help researchers new in this realm to get familiar with the bat-inspired robots by adopting features from existing designs. It also concludes technical challenges associated with future development, involving biological research, aerodynamic modeling, mechanical design, and control technique.

The flow and heat transfer characteristics in a rectangular channel with miniature cuboid dimples

In present work, the miniature cuboid dimples are arranged in specified interval mode along spanwise direction at the bottom of a rectangular channel, and the depth of the miniature cuboid dimples is much less than the thickness of boundary layer. Large eddy simulation (LES) is employed to probe the flow behavior and heat transfer performance of the channel. The numerical results indicate that protrusion effect, secondary vortex, and local miniature air rolling bearing can be induced by miniature cuboid dimples, which result in the drag reduction and slight variation of heat transfer performance in rectangular channel. The simulation results reveal that the existence of the miniature dimples induces additional slip velocity, which effectively reduces the velocity gradient near the bottom of the channel. The low-speed streaks are widened and the mixing of high-speed and low-speed fluids is inhibited. The secondary vortex generated inside the miniature cuboid dimples increases the thickness of the viscous sub-layer, and the rolling friction between the vortex inside miniature cuboid dimples and the upper fluid above the miniature cuboid dimples replaces the sliding friction between wall and fluid. Compared with the channel without miniature cuboid dimples, more than 6% drag reduction can be obtained with the miniature cuboid dimples.

Design, modeling and characteristics research of a novel self-air-cooling reciprocating compressor

High pressure miniature air compressor plays an irreplaceable role in some areas such as refrigeration and pneumatic ejection in the fighter. But, as a lot of heat is produced during compression, the cooling system consumes relatively more energy and its volume and weight even exceeds the compressor itself. So, how to design cooling system becomes the key factor. A novel self-air-cooling reciprocating compressor (SACRC) is first proposed in this paper, which can continuously cool the compressor through ingenious structural design without any auxiliary equipment. The SACRC's model is established considering of thermal dynamics and heat exchange characteristics of all the units, which regards the spiral groove in the cooling system as a chamber with compressibility and heat exchange. The characteristics of SACRC are studied by comparative simulation and experiment validation with traditional reciprocating compressor (TRC). The research results showed that the volumetric efficiency and temperature characteristics of SACRC are both improved significantly especially at high output pressure and long working time, which is pioneering significance to further improve the miniaturization of high pressure compressor.

Design and Verification Test of the Primary and Secondary Stage Compression Gap Size of Miniature High Pressure Air Compressor

Aiming at the prominent problem of the primary and secondary pistons hitting the cylinder head in the current miniature high-pressure three-stage air compressor, a design study on the size of the primary and secondary compression gap is carried out. Through the theoretical force analysis of the connecting rod, the force situation of the connecting rod at the top and bottom dead centers is obtained. The three-dimensional models of connecting rod and cylinder are established based on SolidWorks software, and the three-dimensional models are simplified. Based on Ansys Workbench software, the connecting rod and cylinder models are pre-processed, model discretized and post-processed. The structural statics and steady-state thermal comprehensive analysis of the connecting rod at the upper and lower dead points and the steady-state thermal analysis of the cylinder under working conditions are completed. Through analysis, the total axial deformation of the connecting rod and the total axial deformation of the cylinder under high temperature and high speed working conditions are obtained. According to the simulation analysis results, the first and second compression gap size is designed. Based on the design results, ten sets of air compressors are tested for dimensional design verification. The test results show that the air compressors of each test group can complete the charging requirements within the specified time, and there is no phenomenon of piston hitting the cylinder head, indicating the size design reliability.

Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

In this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

Bio-coated surfaces with micro-roughness and micro-porosity: Next generation coatings for enhanced energy efficiency

Due to growing cooling demands as well as emerging global warming and climate change issues, cooling systems should be more efficiently utilized. Boiling is an effective heat transfer mechanism, which has a critical role in many cooling systems. Surface modification is considered as the major approach for boiling heat transfer enhancement. In this study, we developed a microbial bio-coating surface modification technique for phase change cooling applications. Thermoacidophilic Sulfolobus solfataricus coating was implemented using a facile dip coating method on different metallic and non-metallic surfaces. Controlled by drying conditions, the coating exhibited rough and porous morphologies. When tested in a boiling heat transfer setup, bio-coated surfaces offered enhancements up to 76.3% in Critical Heat Flux (CHF). Next, a miniature evaporator was coated and tested for real-world air-conditioning applications, and coefficient of performance (COP) enhancements up to 11% clearly revealed the potential of bio-coated surfaces for energy saving purpose and reduction in greenhouse gasses. Furthermore, coated evaporators reduced the exergy destruction rate up to 8%. This study not only offers a new type of coating morphology, but the applicability of the proposed bio-coating is also proven in a miniature air conditioning system.

Perancangan Detektor Kebocoran Gas LPG Berbasis Arduino yang Terhubung dengan Smartphone

LPG (Liquefied Petroleum Gas) has become the fuel for cooking for most households in Indonesia. The use of LPG for cooking requires high level of caution, due to the danger that may arise from gas leakage. If the molecules of flammable LPG gas are present in the air at a certain concentration and there is a triggerring factor in the form of flame or sparks, explosion and fire may occur. To prevent disasters caused by a LPG gas leakage, the author proposed an Arduino-based LPG gas leak detector (GLD). The GLD is equipped with a MQ-2 gas sensor, capable of measuring the LPG concentration in air in units of parts per million (ppm). Based on the measurement result, the GLD provides an early warning of LPG leakage through 3 condition levels: Normal, Alert, and Danger. Each condition level is characterized by the activation of LED indicators, a miniature air circulation fan , and a buzzer. Alert warning is released when the sensor reads more than 400 ppm (2.05% of LPG Lower Explosive Level). Danger warning is given at 800 ppm (4.10% of LPG Lower Explosive Level) or higher reading. An HC-06 Bluetooth module creates a wireless connection between the GLD and a smartphone. Through an application created on Blynk platform, the smartphone can monitor the LPG concentration at a distance of 10 m from the GLD. The GLD is tested and succeeded to detect gases coming from an LPG cylinder and from a gas lighter. The GLD also runs perfectly for the designed early warning scheme.

Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR\u2013SVR\u2013ARMA combined model

In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy of the six types of pollutants in the air. First, the main information of factors affecting air quality is extracted by principal component analysis, and then principal component regression is used to give the predicted values of six types of pollutants. Second, the support vector regression machine is used to regress the predicted value of principal component regression and various influencing factors. Finally, the autoregressive moving average model is used to correct the residual items, and finally the predicted values of six types of pollutants are obtained. The experimental results showed that the proposed combination prediction model of PCR–SVR–ARMA had a better prediction effect than the artificial neural network, the standard support vector regression machine, the principal component regression, and PCR–SVR method. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and relative Mean Absolute Percent Error (MAPE) are used as evaluation indicators to evaluate the PCR–SVR–ARMA model. This model can increase the accuracy of self-built points by 72.6% to 93.2%, and the model has excellent prediction effects in the training set and detection set, indicating that the model has good generalization ability. This model can play an active role scientific arrangement and promotion of miniature air quality detectors and grid-based monitoring of the concentration of various pollutants.