Network-assisted particulate matter sensor

Abstract

The present invention relates to a method for updating the current firmware of a particulate matter (PM) sensor, the method comprising: receiving air quality information of a local geographic area associated with the PM sensor by a processor; determining, by the processor, an update firmware for the PM sensor based at least in part on the air quality information; and replacing the current firmware of the PM sensor with the update firmware by the processor.

Description

Technical Field

[0001] The various implementation schemes generally involve sensing particulate matter. Background Technology

[0002] Awareness of air quality and related health issues is growing rapidly. Air quality can be measured by detecting the mass of particulate matter (PM) in a specific volume. Some particulate matter can penetrate the gas exchange areas of the lungs and thus enter the bloodstream. The smaller the particulate matter, the greater the risk of it entering the bloodstream, and therefore the greater the risk of health problems.

[0003] Therefore, organizations such as government agencies are deploying air quality monitoring stations around the world. These stations can be deployed to monitor and measure airborne particulate matter. Data from these stations is usually published on the internet. Some data can be measures of particle mass density, which can be measured as mass per unit volume. For example, the PM2.5 index is a measure of particulate matter with a diameter of less than 2.5 micrometers, and its unit is micrograms per cubic meter.

[0004] All particulate groups have size distributions. For example, cigarette smoke from an exemplary country can be distributed as particles of about 0.1 µm in size. Pollen size distributions can typically fall between about 5 µm and about 75 µm. Furthermore, the size distribution of PM can depend on location, time of day, and many other local environmental conditions. Summary of the Invention

[0005] The apparatus and associated method relate to a particulate matter (PM) sensor assembly that receives PM count values ​​from an optical scattering sensor and selectively calibrates sensor characteristics in response to recently published high-precision air quality information within a local area containing the sensor assembly. In an illustrative example, the air quality information may be generated by various PM monitoring stations and published, for example, as a data stream or aggregate. The sensor assembly may, for example, select a PM mass density reference value for a specific area from the received air quality information associated with the location information of the sensor assembly. Based on the published air quality information, the sensor assembly may select a calibration curve from, for example, a set of predetermined calibration curves. A mobile, low-cost PM sensor assembly can advantageously utilize high-cost published PM air quality information to dynamically improve the accuracy of local PM measurements.

[0006] Various implementations can achieve one or more advantages. For example, some implementations can cost-effectively and with high accuracy measure various particulate matter indices (e.g., PM 2.5, PM 10). Calibration and firmware for various examples can be updated in the field and can be based on continuous updates. Some examples may be located near specific sources of PM pollution (e.g., pollen count). Various examples can be internet-updatable sensors and can be compatible with the Internet of Things (IoT) and Social Internet of Things (SIoT).

[0007] Details of various embodiments are set forth in the accompanying drawings and the following description. Other features and advantages will become apparent from the specification, the accompanying drawings, and the claims. Attached Figure Description

[0008] Figure 1 An exemplary Internet-assisted air quality sensor system is described in an illustrative use case scenario.

[0009] Figure 2 A process flow diagram of an exemplary network-assisted particulate sensor is depicted.

[0010] Figure 3 A graphical view depicting a set of exemplary calibration curves included in an auxiliary particulate sensor.

[0011] Similar reference symbols in the accompanying drawings denote similar elements. Detailed Implementation

[0012] To aid understanding, this document is organized as follows. First, refer to... Figure 1 A brief introduction to an exemplary Internet-assisted particulate matter (PM) sensing system. Next, refer to... Figure 2 The discussion then shifts to an exemplary implementation that illustrates operational details. Finally, Figure 3 A set of exemplary calibration curves is presented, one of which is automatically selected based on its correlation with the agency’s PM reference standard.

[0013] Many low-cost particulate matter sensors can be based on the principle of optical scattering, sometimes called turbidimetry. An air sample containing particles is passed through a detection zone, where light from an interrogating beam is scattered by the particles. A photodetector receives the scattered light as a particle enters the detection zone. In pulse-counting sensors, the increase and decrease in the photodetector signal as a particle enters and is present in the detection zone is called an optical pulse. These pulses are recorded and used as the raw data source for measuring the mass density of particulate matter (PM). During typical factory calibration, particulate matter of known mass density is generated and used to calibrate each sensor. Each sensor records optical pulses per unit time. These calibrations mathematically correlate the optical pulses per unit time with a known PM concentration. This calibration process embeds certain assumptions about particulate matter into the deployed sensors. If real-world particulate matter has the same size distribution, shape distribution, chemical composition, and optical properties as the particulate matter used during calibration, these assumptions are largely correct. However, when any of these (and more) properties of real-world particulate matter differs from those of the particulate matter used in calibration, the assumptions are incorrect and may lead to erroneous mass density readings.

[0014] In at least one exemplary aspect, a calibrated optical pulse counting PM sensor can use the total number of pulses per unit time, which can be calibrated for cigarette smoke (where the vast majority of the mass comes from particles smaller than 1.0 µm in diameter). This PM sensor can be deployed, for example, in the southwestern desert (where typical real-world particulate matter has the majority of the mass from particles larger than 1.0 µm). In such a locally deployed environment, factory calibration of cigarette smoke may result in incorrectly low reported PM mass density (due to the significant difference in particle mass between cigarette smoke and particles in the southwestern desert). Therefore, various implementations may include means and methods for the sensor to receive information about its local environment (e.g., GPS location, PM 2.5 data reported from local EPA stations, etc.) and adjust its calibration coefficients or switch to a separate calibration (curve) more suitable for larger diameter particles. Information about the local environment of a PM sensor can include at least three types of data: (1) direct data about the distribution of particulate matter size (e.g., individual PM2.5 data, combinations of PM2.5, PM10, and PM1, or the ratio of PM2.5, PM10, and PM1), (2) additional air quality data that does not directly contain particulate matter information (e.g., humidity, temperature, atmospheric pressure, rainfall, etc.), or (3) tangential information (e.g., GPS data, whether the windshield wipers are on, whether the vehicle is off-roading, optical information such as images of the surrounding environment, etc.).

[0015] The methods and apparatus described herein can utilize three different types of data to improve the local environmental calibration deployment of PM sensors. These three categories differ in the directness of the relationship between the received information and local particulate matter at the deployed sensor location. As discussed above, these three categories are: (1) direct data on particulate size distribution (e.g., location-specific PM2.5 data alone, or a combination of PM2.5, PM10, and PM1), (2) supplementary air quality data that does not directly contain particulate information (e.g., humidity, temperature, atmospheric pressure, whether it is raining, etc.), and (3) tangential information (e.g., GPS data, whether windshield wipers are on, whether the vehicle is off-roading, optical information of the surrounding environment (images)). Exemplary implementations of calibration adjustments for each of the three types of data are provided below. Some implementations may utilize combinations of the three data categories.

[0016] For Category 1, the deployed sensor will, for example, receive locally published PM2.5 information from its location in the southwestern desert, and compare the received data with PM2.5 readings generated by the sensor based on factory calibration using cigarette smoke. When the difference between the two readings exceeds a threshold, the sensor can switch to calibration, causing the sensor's data to more closely match the locally published value. In some implementations, this can be achieved by moving from calibration curve 325 to calibration curve 305 (see...). Figure 3 ).

[0017] For Category 2, the deployed sensors receive air quality information that does not directly contain particulate matter information. In some implementations, the air quality data may be local humidity. In high humidity environments, particulate matter can adsorb water molecules, which may tend to increase the particle scattering area without increasing the particulate solid mass. Since smaller particles exhibit a larger surface area (and thus exceed the noise floor of typical optical pulse counting PM sensors), high humidity environments can increase the number of optical pulses recorded for a given concentration and size distribution of airborne particles and may lead to erroneous large PM measurements. In some examples, the PM sensor receiving a high humidity value from the environment in which it is deployed may adjust its calibration to produce a lower PM reading.

[0018] For category 3, the deployed sensor can receive tangential information indirectly related to typical particulate matter in the deployment environment. In an exemplary scenario, the sensor can receive location information that identifies the deployment environment as outdoors in the southwestern desert of the United States. In such an environment, airborne particulate matter is predominantly particles larger than 1.0 µm. In some implementations, where it is anticipated that each recorded optical pulse will represent a greater mass than the optical pulses recorded in factory cigarette smoke calibration, the deployed sensor can adjust its calibration from calibration curve 325 to calibration curve 305. Figure 3 ).

[0019] Figure 1 An exemplary internet-assisted air quality sensor system is depicted in an illustrative use case scenario. Use case scenario 100 includes a mobile PM sensor system 105. The mobile PM sensor system 105 may be a personal mobile device (e.g., a phone, tablet, PM sensor) executing a mobile application (app). The mobile PM sensor system 105 is executing an air quality application within a processor 110. The processor 110 receives sensor data 115 from a sensor 120. The sensor 120 is included within an air quality sensor assembly 125. The air quality sensor assembly 125 includes information defining a set of calibration curves 130, which may be stored in non-volatile memory. The processor 110 also receives a signal containing local air quality information 135 via an onboard receiver 140. The processor 110 executes instructions to select a calibration curve from the set of calibration curves 130. One of the selected calibration curves may be based on the air quality information 135, for example, based on a calibration function that has the greatest correlation (e.g., minimum error) with the ambient air quality in the area. Finally, the processor 110 uses the selected calibration curve from the set of calibration curves 130 to determine the particulate matter concentration value 145 based on the processed data from the sensor 120. The measured particulate matter concentration value 145 is displayed to the user on the mobile PM sensor system 105. Therefore, the mobile PM sensor system 105, sensor 120, and the set of calibration curves 130, assisted by air quality information 135, can determine reliable air quality values. The particulate matter concentration value 145 can be an air quality result, which can be a function of the optimal calibration curve from the set of calibration curves 130 and the value from the sensor 120. Various embodiments can advantageously utilize published air quality information to help calibrate the cost-effective PM sensor system 105 in real time.

[0020] A mobile PM sensor system 105 receives local air quality information 135 via a network 150 (e.g., the Internet). The air quality information 135 originates from a local area air quality monitor 155. The local area air quality monitor 155 generates a signal containing local air quality information 135A. The local air quality information 135A is transmitted to the network 150. An air quality server 160 on the network 150 receives the local air quality information 135A. The air quality server 160 can process and format the local air quality information 135A. Furthermore, the air quality server 160 publishes local air quality information 135B. The mobile PM sensor system 105 receives the published local air quality information 135B via the network 150. A receiver 140 within the mobile PM sensor system 105 sends the air quality information 135 to a processor 110.

[0021] A mobile PM sensor system 105 receives air quality information from multiple areas 165. A Global Positioning System (GPS) 170 provides the individual's location 175 to a processor 110. The processor 110 executes an instruction program to select appropriate area-specific air quality information based on the individual's location 175.

[0022] Figure 2 A process flow diagram of an exemplary network-assisted particulate matter sensor is depicted. Particulate matter sensor system (PMSS) 200 includes a PM sensing element 205. The PM sensing element 205 receives a sample 210 of ambient air. For example, the PM sensing element 205 may be an optical pulse counting sensor. The PM sensing element 205 generates a pulse count reading and sends the reading to a PM measurement engine 215.

[0023] Various examples of PM sensing element 205 may include sensing techniques beneficial for detecting large particles, while other PM sensing elements 205 may include sensing techniques beneficial for detecting small particles. In some examples, PM sensing element 205 may be implemented as a photometric sensor. In various embodiments, PM sensing element 205 may employ, for example, a combination of photometric counting techniques and pulse counting techniques. Therefore, PM sensing element 205 may include one or more other types of sensing elements, individually or in combination.

[0024] In some examples, the PM sensing element 205 can be implemented as a weight measurement, a resonator, or a flow-based sensing element. Therefore, various types of PM sensing elements 205 can benefit from auxiliary data, for example. Auxiliary data may include ambient humidity. Ambient humidity can be absorbed by particulate matter, thereby increasing particle mass. For example, when humidity is high, the PMSS 200 can compensate for this effect by selecting a coarser PM calibration curve.

[0025] A regional air quality monitor 220 collects samples of ambient air 210 within the same local geographic area as the PM sensing element 205. The regional air quality monitor 220 can generate various air quality parameters and indices. These parameters and indices are sent to an environmental agency 225. The environmental agency 225 may be included within a group of various organizations related to air quality and / or public health. The various air quality parameters and indices collected from the regional air quality monitor 220 can be combined with data from multiple regional air quality monitors 220 and sent to the environmental agency 225. The environmental agency 225 can generate information related to the data collected by the regional air quality monitor 220. This information can be published on various public networks. In some examples, the information may be published on the Internet 230.

[0026] In various implementations, PMSS 200 can directly access relevant publicly available air quality information (e.g., pollution data) in module 235 from the Internet 230. In some examples, PMSS 200 can indirectly access relevant publicly available air quality information in module 235 via a communication link with smart device 240. In the depicted example, module 235 is coupled to smart device 240 via Bluetooth Low Energy (BLE). In some examples, coupling can be via Near Field Communication (NFC) or Radio Frequency Identification (RFID). In some examples, coupling can be hardwired.

[0027] Module 235 also receives information about the individual's geographic location regarding PMSS 200 via module 245. In response to receiving local area information about PMSS 200 and associated publicly available air quality information, module 235 generates various parameter data, such as particle size distribution, particle mass, and particle reflectance related to the local area of ​​PMSS 200. In some examples, air quality information may be received from publicly available air quality stations or from private high-quality air monitoring stations.

[0028] The parameter output of module 235 is input to module 250, i.e., the calibration measurement module. In module 250, the PMSS 200 measurement system is calibrated and adjusted. For example, in some embodiments, a predetermined family of calibration curves can be stored within the PMSS 200. Each curve in the family of curves can define a transfer function that converts particle counts to particulate matter mass density. In various embodiments, each curve can be associated with a specific range of particle sizes. Therefore, the measurements performed by the calibration adjustment module 250 can be based on, for example, a size distribution, selecting one of the curves in the family of curves.

[0029] In various implementations, each curve may be associated with a range of, for example, particle reflectance, particle color, particle roughness, and / or particle chemical composition. In each parameter example, module 250 may select a curve associated with a parameter value received from module 235, which is specific data contained in pollution information from publicly available air quality information. Once the curve, along with the particle count sensed from PM sensing element 205, is supplied to PM measuring engine 215, PM air quality value 260 can be measured.

[0030] In some implementations, a calibration curve can be selected by receiving mass density from pollution data of publicly available air quality information. In such implementations, the calibration adjustment measurement module 250 can compare the mass density of each curve evaluated at the current average particle count of the sensor. The curve that results in the mass density of the published data that best approximates the current average particle count can be selected as the valid calibration curve.

[0031] In some implementations, a predetermined family of calibration coefficients can be stored within PMSS 200. Each set of coefficients can define a curve, which can define a transfer function that converts particle counts to particulate mass density. As in the example using a family of curves, each set of coefficients can be associated with a specific range of one or more parameters from module 235. For example, the coefficients can be polynomial coefficients.

[0032] In various implementation schemes, one or more parameters may be used to determine calibration adjustments. Some of these parameters determine which calibration curve to select or which set of polynomial coefficients to use. Others of these parameters can provide adjustments to the curve and / or coefficients.

[0033] Various additional parameters can include road vibration. Road vibration can be an indicator of a built-up area, and it can include larger particles, increasing the size distribution of nearby particles. For example, increased road vibration may cause the calibration adjustments within the PMSS 200 to be more biased towards larger particle sizes. In some examples, the PMSS 200 can be combined with a GPS receiver to receive road condition parameters from an electronic map. For example, the electronic map can transmit signals including road parameters such as paved, unpaved, or off-road.

[0034] For example, various additional parameters may include a "wiper on" signal in the vehicle. The "wiper on" signal can provide information about humidity. For example, because wipers may be active when rain is present, ambient air may be saturated with moisture. Therefore, in various implementations, the PMSS 200 can process the "wiper on" signal because it receives humidity data from, for example, published humidity data from the internet. In some examples, the "wiper on" signal can indicate that particles are being washed away from the vicinity. For example, the "wiper on" signal can cause adjustments within the PMSS 200 to be in a lower and / or finer resolution range.

[0035] Therefore, various additional parameters (including temperature, time of day, wind speed, altitude, and barometer readings) can each have a unique effect on particulate matter and thus on PM parameters, as examples rather than limitations. Thus, each of these additional parameters can potentially influence calibration adjustments.

[0036] In some examples, the PMSS 200 can select one of a family of calibration curves. In some implementations, the calibration adjustment measurement module 250 can use one of the curves as a starting point and then adjust based on one or more of a variety of additional parameters.

[0037] In module 245, PMSS 200 determines its personal geolocation. In the depicted example, this can be achieved via an integrated positioning receiver 255 (e.g., GPS), or via an external smart device (e.g., 240) containing the positioning receiver. In the depicted example, module 245 is coupled to smart device 240 via Bluetooth Low Energy (BLE). The integrated positioning receiver 255 can be an implementation of an open database of cellular towers, which can be a cost-effective non-GPS positioning system. The smart device (e.g., 240) can also employ an implementation of an open database of cellular towers.

[0038] The PM measuring engine 215 inputs the final measurement (module 250) after calibration adjustment and the reading from the PM sensing element 205 to determine the air quality value 260.

[0039] Figure 3 A graphical view depicting a set of exemplary calibration curves included in an auxiliary particulate sensor is provided. The predetermined set of calibration curves 300 includes curves 305, 310, 315, 320, 325, and 330. Each of curves 305-330 represents a transfer function between the raw sensor reading and the calibration output mass density value. Curves 305-330 can be stored within a non-volatile memory (e.g., within air quality sensor assembly 125) within the air quality sensor assembly. Figure 1In Project 130), curve 300 can be generated experimentally for a given particle size distribution of a given air sample. Each curve 305-330 represents the transfer function between the raw sensor readings (e.g., particles per cubic meter) and the mass density (e.g., micrograms per cubic meter).

[0040] In an illustrative example, a PM sensing element (such as PM sensing element 205) can read 50,000 particles per cubic meter in a given geographic area. Based on air quality data published by environmental agencies (such as environmental agency 225), obtained from public websites, or from regional air quality monitors (such as regional air quality monitor 220) that monitor nearby ambient air (such as ambient air 210), the particulate matter mass density can be, for example, 500 µg / m³. 3 In response to sensor readings and air quality data from regional air quality monitors, PMSS (such as PMSS 200) can select curve 325, such as... Figure 3 As described in the text. Therefore, various implementation schemes can advantageously utilize published air quality information to dynamically calibrate cost-effective PM sensing elements in real time.

[0041] In some implementations, the PM sensor system can compare its own particulate matter results with, for example, the average of locally published regional results released on the same day. The PM sensor system can measure its PM concentration as higher than the published average air quality data for the same region. In some examples, the PM sensor system can measure its PM concentration as lower than the published average air quality data for the same region. Furthermore, the sensor system can measure the duration of the high / low concentration comparison. These comparisons can be performed in real time, triggering recalibration when the difference exceeds a predetermined threshold. Recalibration may occur when the particle size distribution within the region changes. Therefore, some examples of PM sensor systems can select an appropriate calibration curve based on the best-fit correlation with current and regionally published air quality (particulate matter) data.

[0042] For example, due to significant industrial activity on a headwind, an area may experience an average particle size distribution of 0.5 µm over a period of time (e.g., smog, sulfuric acid). As a result, the particle sensor system can be calibrated for such low-mass particles (e.g., calibration curve 330). At some point, the prevailing wind may change, and the same area may now experience a larger particle size distribution of 5.0 µm (e.g., cement dust, asphalt) due to a headwind construction site. Because the particle sensor system has previously been calibrated for low-mass particles (e.g., calibration curve 330), the equivalent particle count for the larger particles may result in a mass density that is too low. Therefore, the particle sensor system can receive mass density data from a local air quality measurement system via the internet, for example, data higher than that reported by the particle sensor system in this example. In response, the particle sensor system can trigger an internal recalibration process, resulting in the estimation of the mass density of the new particle size distribution using a different calibration curve (e.g., calibration curve 305).

[0043] Each of curves 305-330 can be stored in non-volatile memory (e.g., within the air quality sensor assembly (e.g., 125)). Figure 1 In Project 130), curves can be stored as data points in an array, which can advantageously provide increasingly higher accuracy as more points are added to the array. In some implementations, curves can be stored as polynomial coefficients, which can be memory-efficient and inherently provide interpolation results.

[0044] While various embodiments have been described with reference to the accompanying drawings, other embodiments are also possible. For example, in some examples, various methods can be used to update the firmware and / or calibration data of the PM sensing system. This method can vary depending on, for example, the sensor's conversion principle, the fidelity of publicly available air quality information, and the desired type of improvement in sensor accuracy.

[0045] In some implementations, the PM sensing system can access publicly available data (e.g., PM size distribution, pollen content, particle chemical composition) about particulate matter pollution sources in the area where the PM sensor system is deployed. The PM sensing system can use publicly available data to update its calibration constants, select a calibration curve (from a set of curves saved at manufacturing time), and / or update firmware to display more accurate and precise readings of localized PM pollution in a given area (e.g., a geographic outdoor location, a room, a car).

[0046] In some implementations, the PM sensing system can provide various communication links. For example, the PM sensing system can receive particulate matter information via an internet connection. The internet connection can be hardwired to the system or it can be wireless. In some implementations, a separate internet-connected device (e.g., a laptop, mobile phone, tablet, desktop computer) can acquire internet-specific data and relay the data to the PM sensing system. In some examples, the PM sensing system can include a GPS receiver. Data from the GPS receiver can help the onboard processor determine the most suitable local particulate matter station for comparing data.

[0047] In various implementations, PM sensor systems can be implemented within smart thermostats (such as thermostats) that include a processor executing pre-programmed instructions. In some implementations, the PM sensor system may be part of a Software-as-a-Service (SaaS) solution. Furthermore, PM sensor systems can cover infrastructure that enables connected devices. Various PM sensing systems can manipulate data to obtain information (e.g., trends, predictive analytics, big data).

[0048] In some implementations, the PM sensing system may initiate a calibration update only when paired with a mobile device. In such implementations, the mobile device can access publicly available air quality information and transmit that information to the PM sensor system via a communication channel (e.g., BLE, NFC, RF).

[0049] In some implementations, information from various network-coupled environmental sensors can benefit from received signals that include network environmental information. For example, a gas sensor system can receive signals containing humidity information from a network. The gas sensor system can predetermine the function to combine the humidity information with the gas sensing information. For example, the combination of humidity information and gas information can advantageously produce more accurate results than gas sensor information alone. In some examples, humidity information can be combined with temperature information to produce more accurate temperature results and / or produce thermal index values. In various implementations, atmospheric pressure can be combined with, for example, time of day information and / or wind speed information received from the network connection signal. Thus, wind speed information can be published from an organization equipped with local Doppler radar and can be combined with various environmental sensor information to produce more accurate results. Furthermore, in various implementations, environmental parameters (e.g., wind speed, wind direction, location) received from published sources on the network can be combined with signals from environmental sensing elements to produce more accurate environmental parameter values ​​than signals generated by environmental sensing elements alone.

[0050] In some examples, PM sensor systems can receive signals containing aerial imagery from satellites and / or drones. These systems can combine the aerial imagery with PM sensing information to produce more accurate PM values. In an illustrative example, a dust storm visually captured by a top-mounted drone can transmit a signal containing intensity and location data to a network. A network-assisted PM sensor system can receive this signal with the dust storm's intensity and location data and combine the data with a predetermined function to produce a more accurate particulate matter mass density value at the dust storm's location.

[0051] In one exemplary aspect, an apparatus can estimate a measure of the amount of substance mixed in a gaseous fluid. The apparatus may include a particulate matter (PM) sensor adapted to sample ambient air and generate a local measurement data signal in response to a measurement of the sampled ambient air; and a processor operatively coupled to the PM sensor to receive the local measurement data signal. A first data storage device is operatively coupled to the processor and includes a plurality of predetermined calibration curves associated with the PM sensor, each of the calibration curves defining a calibration function to substantially compensate for non-ideal accuracy of the local measurement data signal around an operating point of the parameter conditions applicable when the ambient air is sampled by the PM sensor. The apparatus may further include: a receiving module operatively coupled to the processor and configured to receive a published reference information signal indicating air quality at a reference air quality sensing station in a geographic area including the PM sensor and the reference air quality sensing station; and a second data storage device operatively coupled to the processor and including an instruction program that, when executed by the processor, causes the processor to perform an operation to automatically calibrate the local measurement data signal in response to the published reference information signal. The operation may include: (i) determining an estimated operating point of the parameter conditions applicable when the ambient air is sampled by a PM sensor from a received published reference information signal; (ii) selecting one of a plurality of predetermined calibration curves based on the determined operating point; and (iii) generating an air quality measurement signal using the selected predetermined calibration curve applied to the local measurement data signal and displaying it on a display device.

[0052] In some implementations, the PM sensor may include an optical pulse counting sensing element, a weight sensing element, a resonator-based sensing element, or a flow-based sensing element. The PM sensor may be portable and operatively coupled to a handheld mobile communication device, wherein a receiving module is formed within the handheld mobile communication device.

[0053] The device may also include a location determination module operatively coupled to provide the processor with information about the geographic location of the PM sensor. The operation may further include: (iv) receiving information about at least one additional air quality sensing station using a receiving module; and (v) selecting one of the reference air quality sensing stations from the reference air quality sensing station and the at least one additional air quality sensing station within a geographic area containing the PM sensor and the reference air quality sensing station.

[0054] When sampling ambient air using a PM sensor, applicable parameters may include the humidity of the sampled ambient air.

[0055] Some aspects of the implementation scheme can be implemented as a computer system. For example, various implementations may include digital and / or analog circuits, computer hardware, firmware, software, or combinations thereof. Device elements may be implemented in a computer program product tangibly embodied in an information carrier, such as in a machine-readable storage device, for execution by a programmable processor; and may be executed by a programmable processor executing a program of instructions to perform the functions of various implementation schemes by manipulating input data and producing output. Some implementations may advantageously be implemented in one or more computer programs executable on a programmable system including at least one programmable processor coupled to receive and transmit data and instructions to a data storage system, at least one input device, and / or at least one output device. A computer program is a set of instructions that can be used directly or indirectly in a computer to perform an activity or produce approximately a result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for a computing environment.

[0056] Suitable processors for executing instruction programs include, by way of example and not limitation, both general-purpose microprocessors and special-purpose microprocessors, which may include a single processor or one of multiple processors in any type of computer. Typically, the processor receives instructions and data from read-only memory or random access memory, or both. The fundamental components of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and memory may be supplemented by or incorporated into an ASIC (Application-Specific Integrated Circuit). In some embodiments, the processor and components may be supplemented by or incorporated into a hardware-programmable device, such as an FPGA.

[0057] In some implementations, each system can be programmed with the same or similar information and / or initialized with substantially the same information stored in volatile and / or non-volatile memory. For example, a data interface can be configured to perform automatic configuration, automatic download, and / or automatic update functions when coupled to an appropriate host device, such as a desktop computer or server.

[0058] In some implementations, one or more user interface features can be customized to perform specific functions. Exemplary implementations can be implemented in computer systems that include a graphical user interface and / or an internet browser. To provide interaction with the user, some implementations can be implemented on a computer having a display device (such as an LCD (liquid crystal display) monitor for displaying information to the user), a keyboard, and pointing devices (such as a mouse or trackball) through which the user provides input to the computer.

[0059] In various implementations, the system may communicate using suitable communication methods, devices, and technologies. For example, the system may communicate with compatible devices (e.g., devices capable of transmitting data to and / or from the system) using point-to-point communication, where messages are transmitted directly from the source to the first receiver via a dedicated physical link (e.g., fiber optic link, point-to-point cabling, daisy chain). Components of the system may exchange information via analog or digital data communication of any form or medium, including packet-based messaging over a communication network. Examples of communication networks include, for example, LANs (Local Area Networks), WANs (Wide Area Networks), MANs (Metropolitan Area Networks), wireless and / or optical networks, and computers and networks forming the Internet. Other implementations may transmit messages via broadcast to all or substantially all devices coupled together through the communication network, for example, by using omnidirectional radio frequency (RF) signals. Other implementations may transmit messages characterized by high directivity, such as RF signals transmitted using directional (i.e., narrow-beam) antennas or infrared signals that may optionally be used with focusing optics. Other implementations can also be implemented using appropriate interfaces and protocols, such as, by way of example and not intended to be limiting: USB 2.0, FireWire, ATA / IDE, RS-232, RS-422, RS-485, 802.11 a / b / g / n, Wi-Fi, WiFi-Direct, Li-Fi, Bluetooth, Ethernet, IrDA, FDDI (Fiber Distributed Data Interface), Token Ring networks, or frequency, time, or code division multiplexing techniques. Some implementations may optionally include features such as error checking and correction (ECC) for data integrity, or security measures such as encryption (e.g., WEP) and password protection.

[0060] In various implementations, a computer system may include non-transitory memory. The memory may be connected to one or more processors configured to encode data and computer-readable instructions, including processor-executable program instructions. The data and computer-readable instructions may be accessible to the one or more processors. When executed by the one or more processors, the processor-executable program instructions may cause the one or more processors to perform various operations.

[0061] In various implementations, the computer system may include Internet of Things (IoT) devices. IoT devices may include objects embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. IoT devices can be used with wired or wireless devices by sending data to another device via an interface. IoT devices can collect useful data and then autonomously allow that data to flow between other devices.

[0062] Some implementations have been described. However, it should be understood that various modifications can be made. For example, advantageous results can be achieved if the steps of the disclosed technology are performed in a different order, or if the components of the disclosed system are combined in a different manner, or if additional components are added to the components. Therefore, other implementations are contemplated.

Claims

1. A method for updating the current firmware of a particulate matter (PM) sensor, the method comprising: The processor receives air quality information for a local geographic area associated with the PM sensor, where the air quality information is sampled by the PM sensor; The processor receives published reference information indicating the air quality at a reference air quality sensing station in the local geographic area associated with the PM sensor; The processor determines the estimated operating point of the parameter conditions applicable to the ambient air sampled by the PM sensor based on the received published reference information. The processor selects one of a plurality of predetermined calibration curves for the PM sensor based on the estimated operating point; The processor determines the updated firmware of the PM sensor based at least in part on a selected predetermined calibration curve; and The processor replaces the current firmware of the PM sensor with the updated firmware of the PM sensor.

2. The method according to claim 1, wherein, Before receiving air quality information, the method also includes: The processor queries a reference data source to request air quality information for the local geographic area.

3. The method according to claim 1, wherein, Air quality information for the local geographic area includes information on the distribution of particulate matter size.

4. The method according to claim 1, wherein, Air quality information for a local geographic area includes at least one of pollen content information or particulate chemical composition information.

5. The method according to claim 1, further comprising: The processor receives at least one of the following: GPS location information, windshield wiper activation information, or off-road driving information.

6. The method according to claim 1, further comprising: The processor determines the sensor parameters associated with the PM sensor.

7. A firmware update system for a particulate matter (PM) sensor, comprising a processor and a non-transient memory, the non-transient memory including processor-executable program instructions configured to use the processor to update the firmware system: Receive air quality information for a local geographic area associated with a PM sensor, wherein the air quality information is sampled by the PM sensor; The processor receives published reference information indicating the air quality at a reference air quality sensing station in the local geographic area associated with the PM sensor; The processor determines the estimated operating point of the parameter conditions applicable to the ambient air sampled by the PM sensor based on the received published reference information. The processor selects one of a plurality of predetermined calibration curves for the PM sensor based on the estimated operating point; The firmware update for the PM sensor is determined at least in part based on a selected predetermined calibration curve; and Replace the current firmware of the PM sensor with the updated firmware of the PM sensor.

8. The firmware update system according to claim 7, wherein, Before receiving air quality information, the non-transient memory and processor-executable program instructions are configured to use the processor to update the firmware of the system: Query the reference data source to request air quality information for the local geographic area.

9. The firmware update system according to claim 7, wherein, Air quality information for the local geographic area includes information on the distribution of particulate matter size.

10. The firmware update system according to claim 7, wherein, Air quality information for a local geographic area includes at least one of pollen content information or particulate chemical composition information.

11. The firmware update system according to claim 7, wherein, Non-transient memory and processor-executable program instructions are configured to use the processor to update the firmware of the system: It can receive at least one of the following: GPS location information, windshield wiper activation information, or off-road driving information.

12. The firmware update system according to claim 7, wherein, Non-transient memory and processor-executable program instructions are configured to use the processor to update the firmware of the system: Determine the sensor parameters associated with the PM sensor.

13. A non-transient computer program product for updating the current firmware of a particulate matter (PM) sensor, the non-transient computer program product including a non-transient machine-readable storage device having processor-executable program instructions stored thereon, the processor-executable program instructions being configured to: Receive air quality information for a local geographic area associated with a PM sensor, wherein the air quality information is sampled by the PM sensor; Receive published reference information indicating the air quality at a reference air quality sensing station in the local geographic area associated with the PM sensor; Based on the received published reference information, the estimated operating point for the parameter conditions applicable to the ambient air sampled by the PM sensor is determined. Based on the estimated operating point, one of several predetermined calibration curves is selected for the PM sensor; The firmware update for the PM sensor is determined at least in part based on a selected predetermined calibration curve; and Replace the current firmware of the PM sensor with the updated firmware of the PM sensor.

14. The non-transient computer program product according to claim 13, wherein, Before receiving air quality information, the processor's executable program instructions are configured as follows: Query the reference data source to request air quality information for the local geographic area.

15. The non-transient computer program product according to claim 13, wherein, Air quality information for the local geographic area includes information on the distribution of particulate matter size.

16. The non-transient computer program product according to claim 13, wherein, Air quality information for a local geographic area includes at least one of pollen content information or particulate chemical composition information.

17. The non-transient computer program product of claim 13, wherein the processor-executable program instructions are configured as follows: The processor receives at least one of the following: GPS location information, windshield wiper activation information, or off-road driving information.

18. The non-transient computer program product of claim 13, wherein the processor-executable program instructions are configured as follows: The processor determines the sensor parameters associated with the PM sensor.

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