Monitoring of Airborne Pollen: A Patent Review

Abstract

Air pollution is recognized by the World Health Organization as the major environmental threat; therefore, air quality is constantly being monitored by monitoring stations. However, the most common atmospheric pollutants being monitored do not include pollen. Among the reasons for the lack of pollen control is that there are different types and sizes of pollen. The largest particles commonly being monitored by air monitoring stations have a maximum aerodynamic diameter of 10 microns, while the aerodynamic diameter of most pollen grains is known to range from 10 to 100 microns. For this reason, most pollen is not being detected by air monitoring stations. For the patents found in a literature review, monitoring pollen concentration in the air requires the discrimination of pollen grains from particulate matter of a similar size, as well as the identification of the type of pollen grains detected, since different pollen types may produce different effects, such as allergic reactions, asthma, and lung cancer, in exposed people. In this work, 15 patent documents regarding pollen monitoring were identified and reviewed using three search engines: Google Patents, WIPO’s PatentScope, and the United States Patent and Trademark Office (USPTO) database. The extracted data from the patents included whether they differentiate pollen type, pollen size, and sensor type and whether they provide real-time data. The results show that 93.33% of the patents identify pollen type, while 80% of the patents identify pollen size. Most of the patents use light-scattering and image sensors and use image processing techniques to analyze particles. Furthermore, 40% of the patents were found to implement artificial intelligence. Further, it was found that only nine patents provide real-time data, which is an important feature of an air monitoring system.

1. Introduction

Pollen grains are tiny egg-shaped male cells (10–100 μm) created by certain plants as part of their reproduction process that travel through the air and are important allergens that may elicit allergic reactions and other negative effects, such as fever and asthma, in susceptible exposed individuals [1,2,3,4]. By 2007, 13 million people visited medical facilities every year due to allergies [5]. Furthermore, pollen grains can carry pollutants on their outer layer (exine), with dimensions smaller than the grain itself [6]. When the exine ruptures, subpollen particles (SPPs) containing pollen allergens or submicrometric pollen fragments may be released. SPPs range between <0.25 and 2.5 μm in diameter, while submicrometric pollen fragments range between 0.25 and 1.0 μm [4,6,7]. SPPs can adhere to pollutants such as particulate matter (PM), including PM_2.5 (aerodynamic diameter ≤ 2.5 μm) and PM_1.0 (aerodynamic diameter ≤ 1.0 μm), resulting in co-exposure to allergens and pollutants, which can trigger severe allergic responses in susceptible individuals [6].

Air pollution has been recognized as the major environmental threat since the Air Quality Guidelines update in 2006 by the World Health Organization (WHO) was issued [8]. According to the WHO, air pollution is responsible for the death of 13 people every minute due to lung cancer, heart disease, and stroke [9].

Since the presence of pollutants in the atmosphere causes health diseases such as asthma, lung cancer, and heart diseases in exposed people, the increase in air pollution levels in the last several decades represents a global environmental concern [10,11,12,13]. Actions that governments and organizations have taken in response to this problem include the installation of air quality monitoring stations [14,15,16]. The American Academy of Allergy Asthma & Immunology provides a map on which the locations of air monitoring stations that monitor pollen in the United States, Canada, Argentina, China, and the United Arab Emirates are indicated [17]. Air quality monitoring stations are used to collect information regarding the concentration of air pollutants such as PM_2.5, PM₁₀, ozone (O₃), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and carbon monoxide (CO) [18,19,20,21,22]. However, these stations do not count pollen grains, SPPs, or submicrometric pollen fragments suspended in the air that affect the health of people susceptible to pollen, causing allergies, rhinitis, asthma, and even lung cancer in some cases [23,24,25]. Currently, there is no standardized threshold or risk assessment that establishes permissible limits to avoid negative effects on the population susceptible to these allergens, despite knowledge of the impacts on human health that were mentioned above [25,26,27].

A reason why most pollen is not being detected by the majority of air monitoring stations is the aerodynamic diameter of pollen grains, most air monitoring stations detecting particles as big as PM₁₀ (particles with aerodynamic diameters equal to or less than 10 microns) [28,29]. Additionally, monitoring pollen requires being able to identify its type, since different pollen variants may present different effects on human health [30].

The WHO, in [8], mentions the need to research the effects of multiple exposures to these pollutants in the presence of pollen to determine the outcome of such interactions and their effect on human health. Some studies [8,31,32,33] mention that pollen grains are physically, chemically, and biologically modified when in contact with air pollutants, resulting in the decomposition of pollen grains and the generation of smaller pollen particles which are more allergenic, representing a greater risk to the population susceptible to pollen.

In [34], the pollen season is defined as the period of time in which a given amount of pollen is present in the environment. These time periods are defined based on previous years’ pollen concentration records. Traditional pollen quantification has been performed manually by obtaining samples from Hirst-style traps, which have been the standard for pollen monitoring for a long time. However, these instruments are susceptible to presenting inaccurate counts and incorrect pollen type identifications due to human error, and they do not provide real-time data [35,36].

To overcome these limitations, light-scattering sensors have been used together with image sensors to detect pollen concentrations and identify pollen types automatically.

Light-scattering sensors are sensors that emit light from a source to a detector to measure the amount of light that reaches the detector and identify the concentration of particles according to the amount of light scattered by them [36,37].

Most pollen sensors have similar features in their structure and operation, which can be categorized in four stages: (1) pollen capture, (2) pollen monitoring, (3) data transmission, and (4) sample release (see Figure 1). In the pollen capture stage, two of the most commonly used methods are air-flow and gravity pollen capture. In the pollen monitoring stage, one or more sensors with different types of technology are used to count and identify pollen grains, such as camera sensors, light-scattering sensors, acoustic sensors, and biomarkers. In the data transmission stage, pollen monitors can use one or more methods to transmit and display data; the most common data transmission media are USB cables, Wi-Fi, and Bluetooth, and for data display, a computer, a smartphone, or a display built into the monitor itself is used. The sample release stage is related to the pollen capture stage; usually, if an air flow is used for pollen capture, it is also used for sample release, whereas if a sample is captured by gravity, it usually requires a user to manually remove the sample from the device.

A current trend is the implementation of artificial intelligence in pollen monitoring, reducing the time required and increasing the accuracy in identifying pollen and making pollen concentration predictions [37,38,39,40]. Several approaches have been made to monitor pollen in the air.

Table 1. Examples of commercial pollen sensors.

Model/Name	Manufacturer	Sensing Principle	Real-Time Sampling	Uses I.A.
APS400 Particulate Sensor [41]	Pollen Sense LLC, Provo, the United States	Spore-trap style collection	Time intervals	Yes
Captador Polen Burkard [42]	BSG Ingenieros S.L., Valencia, Spain	Spore collection	No	No
DSM501A [43]	SAMYOUNG S&C Sensible Sensing Solutions, Seongnam, South Korea	Light scattering	Yes	No
KH-3000-01 [44]	Yamatronics Corporation, Kanagawa, Japan	Light scattering	Yes	No
OPC-N3 Particle Monitor [45]	Alphasense, Braintree, the United Kingdom	Light scattering	Yes	No
Pollen Monitor BAA500 [46]	Helmut Hund GmbH, Wetzlar, Germany	Light scattering	Yes	Not mentioned
PS2 Pollen Sensor [47]	Shinyei Technology CO. LTD., Kobe, Japan	Light scattering	Yes	No
Sensio Air V3 [48]	Sensio Air, London, England	Light scattering	Yes	Yes
Swisens Poleno Mars [49]	Swisens, Emmen, Switzerland	Light scattering	Yes	Yes
Swisens Poleno Jupiter [50]	Swisens, Emmen, Switzerland	Light scattering	Yes	Yes
Wideband Integrated Bioaerosol Sensor [51]	Droplet Measurement Technologies, Longmont, the United States	Light scattering	Yes	No

shows examples of currently available commercial pollen monitoring devices.

Although reviewing the scientific literature is not the objective of this work, examples of pollen monitoring device developments found in the scientific literature are shown in

Table 2. Examples of publications in scientific journals and congresses about pollen sensors.

Title	Year of Publication	Pollen Sensing Principle	Real-Time Sampling	Uses I.A.
A laboratory evaluation of the new automated pollen sensor beenose: pollen discrimination using machine learning techniques [52]	2023	Light scattering	Yes	Yes
In-flight sensing of pollen grains via laser scattering and deep learning [53]	2021	Light scattering	Yes	Yes
A new portable sampler to monitor pollen at street level in the environment of patients [54]	2020	Pollen collector	No	No
Automated pollen detection with an affordable technology [55]	2020	Pollen collector	No	Yes
Ultraviolet laser-induced fluorescence lidar for pollen detection [56]	2017	Light scattering	Not specified	No
Automated pollen monitoring system using laser optics for observing seasonal changes in the concentration of total airborne pollen [57]	2017	Light scattering	Yes	No
All-optical automatic pollen identification: towards an operational system [58]	2016	Light scattering	Yes	Yes

.

The goal of this research is to present the overall state of the patent literature in the realm of pollen monitoring through a comprehensive patent review process in order to complement previous scientific literature reviews and provide a wider overview of this field’s technological state.

2. Methodology

To identify the trend that pollen monitoring has followed in the last 10 years, the temporal distribution from 2014 to 2023 of research papers in this field was obtained by inputting the string “pollen monitor” in all fields in the Web of Science database. The results of the search were filtered by year to identify the number of papers that were published yearly during the last 10 years. This was carried out additionally to the methodology of the patent review process.

To perform the present patent review, the methodology presented in Figure 2 was proposed, whose stages will be described in detail in the following sections.

2.1. Patent Review Article Search Methodology

The search for patent review articles in the field of pollen monitoring was first made in SCOPUS, IEEEXPLORE, and Google Scholar.

The searches in Google Scholar were made using the following query strings: ALL (“pollen sensor” AND “patent review”), ALL (“pollen count” AND “patent review”), and ALL (“pollen monitoring” AND “patent review”). Zero results were obtained.

The searches in IEEEXPLORE were made using the following query strings: ALL (“pollen sensor” AND “patent review”), ALL (“pollen count” AND “patent review”), and ALL (“pollen monitoring” AND “patent review”). The searches output zero results.

Finally, the searches in SCOPUS were made using the following query strings: ALL (“pollen sensor” AND “patent review”), ALL (“pollen count” AND “patent review”), and ALL (“pollen monitoring” AND “patent review”). Zero results were obtained.

This article will provide useful information to improve the monitoring of airborne pollen particles, since no patent review articles on pollen sensors were found.

2.2. Data Sources and Search Strategy

Several searches were conducted to find patents on pollen sensors from the last 60 years. The searches were made using six different input query strings that were adapted to each search engine. The searches were made in all fields in each search engine. Every search was made in three patent search engines: Google Patents, the World Intellectual Property Organization (WIPO)’s PatentScope, and the United States Patent and Trademark Office (USPTO). Google Patents and WIPO’s PatentScope were selected because both include patents from patent offices worldwide, while the USPTO patent search tool was selected since it includes an advanced search interface that facilitates patent identification by allowing results to be grouped by family and shows results in a table that allows patent metadata to be transferred to a spreadsheet.

The searches were conducted by defining two blocks of Medical Subject Heading (MeSH) terms: blocks of keywords were defined for pollen counting: “pollen sensor”, “pollen monitoring”, and “pollen count”; the keywords used to refer to pollen were “pollen grain” and “airborne”, as well as the word “pollen”. Subsequently, the MeSH terms were grouped using the Boolean operator “AND” to define six input query strings: (1) “pollen monitoring” AND “pollen sensor”, (2) “pollen monitoring” AND “pollen count”, (3) “pollen count” AND “pollen sensor”, (4) “aerobiology” AND “pollen”, (5) “aerobiology” AND “monitoring”, and (6) “pollen grain” AND “sensor”.

2.3. Inclusion and Exclusion Criteria

The studies were selected by defining the inclusion and exclusion criteria shown in

Table 3. Inclusion and exclusion criteria.

Inclusion	Exclusion
Pollen count is the main objective	Pollen concentration is not measured
Pollen count is as relevant as other variables	The features of the invention are not described in detail in the document
All patents screened regardless of language

.

2.4. Study Selection

A similar methodology to the Preferred Reporting for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used to carry out the study selection process [59].

Figure 3 shows a flowchart of the stages that were carried out in this section. The patents were first identified, and removal of duplicated patents took place after to consolidate results.

2.5. Data Extraction

The data that were extracted from every reviewed patent were the patent number; the title of the invention; the status; the application date; the name(s) of the inventor(s); the applicant; whether pollen type is identified; whether pollen size is identified; the sensor type; the detected particles and parameters; how information is displayed or provided; whether fixed or mobile sensors are presented; whether indoor or outdoor sensors are presented; whether real-time data are provided; whether sensor calibration is specified; and, if implemented, the subfield of artificial intelligence used (deep learning or machine earning). The extracted data were selected in order to identify the most relevant features that would allow the identification of the current technological state of patents in the pollen monitoring field to provide a starting point to generate new technology and enhance current technology in the field.

3. Results

Monitoring pollen in the air has been a growing trend in recent years due to a general interest in air quality monitoring. Figure 4 shows the temporal distribution of results obtained for the last 10 years from the Web of Science database when the search string “pollen air monitor” was input in all fields. The search output 26 results from 2014, 18 results from 2015, 26 results from 2016, 26 results from 2017, 35 results from 2018, 41 results from 2019, 40 results from 2020, 29 results from 2021, 45 results from 2022, and 45 results from 2023. The years when most documents were published according to the Web of Science database were the last two years (2022 and 2023), and the lowest number of publications in a year during the last 10 years occurred in 2015. The highest increase in a single year was from 2021 to 2022, when the increase was 55.17%, while the increase in the number of publications in 2023 with respect to 2014 was 73.08%.

The total number of patents identified from the searches was 1810, as shown in Figure 3. In

Table 4. Patent search strategy results.

Source	Input Query String
	“pollen grain” and “sensor”	“aerobiology” and “monitoring”	“aerobiology” and “pollen”	“pollen count” and “pollen sensor”	“pollen monitoring” and “pollen count”	“pollen monitoring” and “pollen sensor”
Google Patents	153	37	31	26	3	3
WIPO’s PatentScope	682	96	67	70	2	4
USPTO	514	34	31	50	3	4

, the number of documents from each search engine is presented. A total of 253 patents were identified by Google Patents, 921 patents were identified by WIPO’s PatentsScope, and 636 patents were identified by USPTO. A total of 1292 documents were found to be duplicates from the combined search including all search engines and were removed. The remaining 518 patents were included in the screening phase. Finally, 15 patents met the inclusion criterion shown in Table 3 and were included in this review.

3.1. Patent Analysis

The patent office of origin of every reviewed patent is shown in Figure 5. According to the metadata, the United States holds the leading position in pollen monitoring research, with a patent ratio of 40%. Europe holds the second position in pollen monitoring research, with a patent ratio of 20%. And 13.33% of patents regarding pollen monitoring are filed under the International Patent System. Taiwan, Japan, China, and Canada are each the origin of 6.66% of patents in pollen monitoring. Figure 6 shows the geographical distribution of patents, in which the specific country of origin is shown in order to include patents filed under the International Patent System and those filed under the European Patent Office.

Regarding the status of the 15 reviewed patents, 7 patents (46.67%) are currently active, 3 have expired (20%), 3 remain pending (20%) due to the revision process of the office where the application was made, 1 (6.67%) was withdrawn, and 1 (6.67%) was abandoned before it was granted.

Every patent is grouped under at least one section of the International Patent Classification established by the WIPO. The sections in which patents in this review are grouped are as follows: section A, corresponding to Human Necessities; section B, corresponding to performing operations and transportation; section F, corresponding to mechanical engineering, lighting, heating, weapons, and blasting; section G, corresponding to physics; and section H, corresponding to electricity in the International Patent Classification. The classification of the reviewed patents is shown in

Table 5. Patent classification according to the International Patent Classification.

Section	Class	Subclass	Group	Subgroup	Patents
A Human necessities	A61	A61B	A61B5	A61B5/00	US11490852B1 [60]
		A61L	A61L2	A61L2/00	US11490852B1 [60]
B Performing operations and transportation	B03	B03C	B03C3	B03C3/36	US20230358661A1 [61]
				B03C3/45	US20230358661A1 [61]
F Mechanical engineering, lighting, heating, weapons, and blasting	F24	F24F	F24F11	F24F11/6G4	JP7518413B2 [62]
G Physics	G01	G01B	G01B11	G01B11/24	US11698331B1 [63]
					WO2008063192A1 [64]
				G01B11/30	WO2008063192A1 [64]
		G01N	G01N1	G01N1/22	US11698331B1 [63]
					US20230358661A1 [61]
				G01N1/24	US11698331B1 [63]
				G01N1/40	US20230358661A1 [61]
			G01N15	G01N15/00	US11698331B1 [63]
					CN105388093B [65]
					EP3605059A1 [66]
					EP1408321B1 [67]
				G01N15/02	US11698331B1 [63]
					US20230358661A1 [61]
					EP1408321B1 [67]
					EP4085246B1 [68]
				G01N15/06	US11946850B2 [69]
					CA3183076A1 [70]
					CN105388093B [65]
					US20230358661A1 [61]
					EP3605059A1 [66]
					JP7518413B2 [62]
					US11698331B1 [63]
					EP4085246B1 [68]
				G01N15/10	EP3605059A1 [66]
				G01N15/14	US11946850B2 [69]
					US20150355084A1 [71]
					US20230358661A1 [61]
					EP1408321B1 [67]
					EP3605059A1 [66]
					WO2023110716A1 [72]
					US11698331B1 [63]
				G01N15/1434	US11946850B2 [69]
			G01N21	G01N21/21	EP1408321B1 [67]
				G01N21/47	EP4085246B1 [68]
				G01N21/53	EP4085246B1 [68]
				G01N21/85	EP4085246B1 [68]
				G01N21/3563	US20150355084A1 [71]
			G01N27	G01N27/26	TW200804801A [73]
			G01N33	G01N33/00	US20230358661A1 [61]
					CA3183076A1 [70]
				G01N33/49	US11698331B1 [63]
				G01N35/00	US11698331B1 [63]
	G03	G03H	G03H1	G03H1/08	US11946850B2 [69]
	G06	G06F	G06F18	G06F18/24	US20230358661A1 [61]
		G06T	G06T7	G06T7/11	US20230358661A1 [61]
		G06V	G06V20	G06V20/69	US11698331B1 [63]
	H01	H01L	H01L21	H01L27/00	US8492172B2 [74]
			H01L27	H01L27/14	US8492172B2 [74]
H Electricity	H04	H04N	H04N23	H04N23/56	US20230358661A1 [61]
				H04N23/67	US20230358661A1 [61]
				H04N23/74	US20230358661A1 [61]

. Most patents (93.33%) are grouped under section G, and 92.86% of those patents are grouped under subclass G01N, which encompasses patents related to investigating or analyzing materials by determining their chemical or physical properties, involving enzymes or microorganisms. Sections A, B, and H group 6.67% of patents each.

It was identified that 14 of the 15 reviewed patents (93.33%) identify pollen type and that most of them do so automatically using a pollen database stored in the presented device or in a remote server, together with image processing techniques, while some patents require the user to manually select the pollen type after providing them with information regarding the detected pollen particles. It is not specified in one patent whether pollen type may be identified or not. Twelve patents (80%) are able to provide information regarding particle size, while two patents (13.33%) are not, and one patent (6.66%) does not specify whether particle size may be identified.

Only one patent (6.66%) presents a mobile device, which corresponds to a wearable device that may be used as a wristband.

Regarding the type of sensor, seven sensors found in patents (46.66%) are optical sensors that work under the light-scattering principle to measure pollen concentration, while four patents (26.66%) use a camera to identify pollen concentration by counting particles using image processing techniques. Nine patents (60%) use a combination of both a camera and a light-scattering sensor. Additionally, one patent (6.66%) uses a field-effect transistor (FET)-based pollen sensor, and one patent uses a microelectronic mechanical system (MEMS)-based sensor.

Five patents (33.33%) were found to detect pollen grains exclusively, while the rest of them (66.66%) may detect additional substances and/or materials besides pollen, such as microorganisms, dust, and meteorological and environmental conditions.

Four patents (26.66%) show the output information in an integrated display, while the rest of the patents (73.33%) require an external device such as a computer or a smartphone to communicate and display information.

Only five patents (33.33%) specify whether the device is meant to be used indoors, outdoors, or both. Of these, four patents (26.66%) mention that the device is suitable for both, while one patent (6.66%) mentions that the device is meant to work indoors.

Nine patents (60%) present a device that is able to provide real-time data, and seven patents (46.66%) implement artificial intelligence, five of which (33.33%) use machine learning and one of which (6.66%) uses deep learning.

Only two patents (13.33%) specify the procedure to calibrate the device.

3.2. Patent Summary

3.2.1. Patent EP1408321B1, Europe, 2003

This invention describes a sensor that takes advantage of the fact that pollen particles have a different degree of light polarization than other particles, even ones that are similar in size. The sensor contains a light emitter that emits polarized light in a specific direction and contains multiple receivers with different orientations to detect the intensity of light in different directions after being scattered by the suspended particles. This way, the amount of pollen suspended in the air can be determined.

3.2.2. Patent TW200804801A, Taiwan, 2006

This invention describes a pollen concentration detection targeting device that utilizes carbon nanotube transistors. This device uses carbon nanotubes, which are configured as transistors with a channel that can detect and measure pollen concentrations by tracking changes in electrical resistance. By coating the nanotubes with specific proteins derived from pollen stigmas, the device achieves high specificity for different types of pollen. When pollen grains attach to the nanotubes, they alter the electrical resistance due to protein binding, enabling the device to accurately detect even individual pollen grains.

3.2.3. Patent WO2008063192A1, International, 2006

This invention aims to solve a drawback of the previous inventions, namely, that pollen grains and similar particles cannot be correctly identified since they are identified according to particle size using light-scattering techniques and light-polarizing properties of the analyzed samples, such that they can be misidentified as other particles of a similar size. The present invention provides an apparatus that, by utilizing a database containing pollen information, is able to identify types of pollen by including a camera sensor and utilizing image recognition techniques. The device collects particles on a plate by charging them or with an optical tweezer. The camera sensor records images of the collected particles and, through image recognition software, attempts to identify the collected samples. If the particles are successfully identified, the software shows the source of the particles (e.g., the plant that emits a specific pollen type). If the particles are not successfully identified, the software displays an image of the collected samples and prompts the user to manually identify the particles.

3.2.4. Patent US8492172B2, United States, 2013

This invention comprises a compact sensor designed to detect airborne particles (e.g., pollen) by means of a microelectromechanical system (MEMS) structure. The device comprises a sensor featuring a pair of electrodes arranged in a bridge structure that can detect the presence of particles by physical contact or separation; thus, when a particle is adsorbed in a sensing orifice, the electrodes come into contact or separate, allowing the sensor to determine if a particle is present by measuring changes in electrical conductivity. The device design is small and efficient, as well as cost-effective, due to the use of thin-film materials on insulating substrates, allowing its use in applications such as pollution monitoring. Because the device is MEMS-based, this invention features faster detection times and a smaller device size compared to conventional optical methods, which are typically bulkier and slower. In addition, the invention features a communication module to transmit data wirelessly, enabling real-time updates on air quality or particle levels in various environments, as well as the creation of databases located in different locations to identify the type of pollen, generating a map of the types of airborne pollen.

3.2.5. Patent US20150355084A1, United States, 2013

This invention provides an apparatus capable of identifying particulate matters of different sizes, including pollen grains. The apparatus contains a collection device that consists of a transparent collection area, a positioning element that optimizes the placement of particulate matter, an identification element that consists of an illumination source, and an analysis component, where the illumination source is capable of emitting light of different wavelengths, such as visible light to facilitate particulate matter observation, ultraviolet to make particulate matter fluoresce, and infrared to identify particulate matter constituents, while the analysis component may contain optical detectors, such as microscopes, spectrometers, and/or cameras, for particulate matter inspection. The collection device communicates with an electronic device such as a smartphone or a computer to send and receive library stored data for particulate matter identification through size comparison.

3.2.6. Patent CN105388093B, China, 2015

This invention describes an online airborne pollen monitoring system comprising an impact sampler, a light microscope, a sampling film band, a transmission turn axis, a camera, a sampling pump, and an industrial personal computer. The impact sampler collects pollen particles in a sampling film band. The sampling film band is observed by the camera through the microscope to acquire an image and transmit it to the industrial personal computer, and image processing software is used to analyze the acquired image and judge the pollen type and quantity.

3.2.7. Patent EP3605059A1, Europe, 2018

This invention presents a particle and pollen sensor designed to identify and measure various types of airborne particles, including pollen, dust, and organic and inorganic particles. The device operates by monitoring relative humidity in a region and measuring particle size; by analyzing changes in particle size and properties, the system can differentiate between different types of particles. The device includes a humidity control system, a particle analysis system, and a processor for identifying particle types. The particle analysis can be performed using optical scattering techniques, and the sensor can be configured with multiple processing chambers to subject the particles to different moisture levels.

3.2.8. Patent US11698331B1, United States, 2020

This invention presents an airborne biological particle surveillance device that features a dual imaging system with a standard illumination source and a quantum-dot illumination source. This dual illumination approach enables detailed particle identification and classification by capturing high-resolution images under different illumination conditions. In addition, the device integrates connectivity features, including a network interface for real-time data transmission and access to contextual information from a remote server. This capability improves the accuracy of particle identification by incorporating geographic and environmental data. Additionally, the device features a portable battery and a removable collection cartridge.

3.2.9. Patent US11946850B2, United States, 2020

This invention aims to provide an airborne particulate matter detector that emits light from a light source to detect the amount of light scattered, reflected, and deviated (including its direction) by particles in an airflow using photosensitive detectors. This way, the device acquires information to reconstruct particles, for example, pollen grains and bacteria, as three-dimensional images to analyze and identify them.

3.2.10. Patent EP3605059A1, Europe, 2020

This invention aims to provide a device that avoids previous inventions’ drawbacks, including the need for expensive devices and the lack of real-time measurements. The presented device uses four photosensitive sensors that detect pollen at a combined range from 5 to 175 degrees to detect the amount of scattered light and the diffusion angle, allowing the determination of the concentration and type of pollen in the surrounding air, since it is known that different types of pollen grains diffuse light at different angles. Using four sensors to detect pollen concentrations at different angles allows the determination of pollen propagation in the air. The device communicates with a remote server to almost instantly transfer the measured data, such that it is able to provide real-time data regarding the pollen concentration and pollen type surrounding the device. A set consisting of many of the presented devices and a remote server is able to determine the probability of pollen appearance in the area surrounding a specific device when the remote server sends meteorological data to that specific device, as well as data measured by other devices in the set.

3.2.11. Patent CA3183076A1, Canada, 2021

This invention features a method for detecting aerosol particles in ambient air by means of a photoacoustic gas sensor, where an analysis volume is present in the beam path of a modulatable emitter such that the emitter can use modulatable radiation to excite aerosol particles in the analysis volume to form acoustic pressure waves that are detectable by means of the sensor. Using the modulatable emitter, the analysis volume is irradiated with the modulated radiation to generate acoustic pressure waves. The generated acoustic pressure waves are measured by means of the sensor, whereby the presence and/or concentration of the aerosol particles in the ambient air is determined on the basis of the measurement results.

3.2.12. Patent US11490852B1, United States, 2021

This invention aims to provide a wearable device that supplies information regarding air quality, environmental conditions, and microorganism concentrations without the necessity of previously collecting a sample. The wearable device may be worn as a wristband, a necklace, a belt, or a headband, and includes a display unit where the mentioned information is shown. The device comprises three sensors: a microbial biosensor which detects and counts microorganisms and pathogens; a particulate matter sensor which detects and monitors particles in the surrounding air, including pollen; and an in enviro sensor which detects and monitors environmental conditions surrounding the device. Pollen is detected by light scattering and particle imaging to determine its shape, size, and number of apertures and compare these data with known pollen grain information from a database, allowing correct identification. The device also includes a set of pollen safety data sheets for different pollen types that can be viewed on the display unit when the corresponding pollen type is detected. The device sends the detected information to a cloud server and may communicate with a remote device such as a computer or a smartphone to display the detected parameters.

3.2.13. Patent JP7518413B2, Japan, 2022

This invention provides a method to predict the amount of pollen in an entire space using a single pollen sensor and a dust sensor, aiming to solve a problem present in previous inventions, namely, that the pollen concentration parameter is only known in the exact location where a pollen sensor is located and particles located away from the sensor cannot be detected, by using a pollen sensor, a dust sensor, and a machine learning approach to obtain a statistical distribution previously generated based on the amount of dust floating in the air in a given space. This way, the distribution of pollen in the air in a given space may be predicted by detecting both the pollen and dust concentration in a specific location inside the given space.

3.2.14. Patent WO2023110716A1, International, 2022

This invention provides an apparatus capable of detecting pollen and mold spores using a concentration device that contains an air inlet that captures air and concentrates pollen and mold spores to illuminate them with a laser light beam that generates holographic images and allows the detection of the amount of light scattered by the suspended particles in the air sample and fluoresces them to visualize them using a lensless microscope and a camera. Using an image processing unit, the particles in the holographic images are detected and counted.

3.2.15. Patent US20230358661A1, United States, 2023

This invention comprises a collection system, a light and imaging system, a release and cleaning system, and an analysis method. The collection system contains an air-flow mechanism to draw ambient air into the device through an aperture. After entering the device, an air chamber receives the air and passes a negatively charged electrode that negatively charges the airborne particles. An electric field generator generates a positive electric field that attracts the negatively charged particles to collect them on a deposition surface. The lighting and imaging system has the purpose of capturing, recording, and storing images of sufficient quality for identification and analysis of the collected particles using a camera or any suitable sensor associated with light emission that emits light with a wavelength range from 200 to 800 nanometers. By detecting the amount of light diffused by the sample, topological features may be inferred. The release and cleaning system may be able to reverse the electric charge of the particles to eject them from the chamber or use any other physical mechanism to remove the particles from the chamber. The analysis method allows the acquired airborne particles to be identified by processing the acquired images using a computer or a server processor, and image processing may comprise three-dimensional reconstruction from lighting, image compositing, or correction.

The list containing the extracted data as indicated in Section 2.5 of patents that met the inclusion criteria is shown in filed date chronological order in

Table 6. List of patents that met the inclusion criteria. Extracted data from patent records.

ID	Patent #	Title	Status	Application Date	Inventor(s)	Applicant	Pollen Type Detection	Pollen Size Detection	Sensor Type	Detected Particles and Parameters	Device Used to Display Data	Fixed/ Mobile	Indoor/Outdoor Monitoring	Calibration Process	Provides Real-Time Data	Artificial Intelligence Implementation
A	EP1408321B1 [67]	Pollen sensor and method	Expired	2 October 2003	Satoshi Okomura, Toyohiro Usui, and Toshiaki Iwai	Shinyei Corp	No	No	Optical sensor (light scattering)	Pollen	External device	Fixed	Not specified	Not mentioned	Yes	Not implemented
B	TW200804801A [73]	A detection mechanism of pollen’s concentration	Active	13 July 2006	Jung-Tang Huang, Chao-Heng Chien, and Yi-Ching Lin	Kuender & Co Ltd.	Yes	No	Field-effect transistor-based pollen sensor	Pollen	External device	Fixed	Not specified	Not mentioned	Yes	Not implemented
C	WO2008063192A1 [64]	Pollen sensor	Active	22 November 2006	Kishimoto Nakamura, Norio Nakamura, and Yuri Kishimoto	Optoelectronics Co., Ltd., Opticon, Inc.	Yes	Yes	Optical (light scattering) and camera sensor (CMOS, CCD)	Pollen	Integrated display	Fixed	Not specified	Not mentioned	No; typically, a 24 h waiting time, though this is adjustable	Not implemented
D	US8492172B2 [74]	Particle detection sensor, method for manufacturing particle detection sensor, and method for detecting particle using particle detection sensor	Expired	23 July 2013	Yamaguchi; Mayumi, Izumi; Konami, Tateishi; and Fuminori	Semiconductor Energy Laboratory Co., Ltd.	Yes	Yes	Particle detection sensor (MEMS sensor)	Airborne particulate matter	External device	Not specified	Not specified	Not mentioned	No; specifies a short waiting time	Not implemented
E	US20150355084A1 [71]	Optimizing analysis and identification of particulate matter	Abandoned	18 December 2013	Richard M. White	University of California	Yes	Yes	Optical, camera (CMOS), and/or photoacoustic sensors	Pollen and particulate matter	External device	Fixed	Indoor and outdoor	Not mentioned	No; waiting time not specified	Not implemented
F	CN105388093B [65]	The on-line monitoring system of pollen in a kind of air	Expired	2 November 2015	Zeng Limin, Xu Xunan, and Li Zailing	Peking University	Yes	Yes	Camera sensor (CCD)	Pollen	External device	Not specified	Not specified	Not mentioned	Yes	Not implemented
G	EP3605059A1 [66]	Particle sensor and sensing method	Withdrawn	31 July 2018	Shuang Chen, Tao Kong, and Paul Van Der Sluis	Koninklijke Philips NV	Yes	Yes	Optical sensor (light scattering) and a humidity control system	Pollen, dust, organic and inorganic particles	Not specified	Not specified	Not specified	Not mentioned	Yes	Not implemented
H	US11698331B1 [63]	Airborne particle monitoring system with illumination and imaging	Active	16 December 2020	Pedro Manautou, Joel Kent, and An-chun Tien	Scanit Technologies Inc.	Yes	Yes	Camera sensor	Pollen	Integrated display	Fixed	Indoor and outdoor	Not mentioned	Yes	Machine learning
I	US11946850B2 [69]	Device for detecting particles including pollen in air using digital holographic reconstruction	Active	18 December 2020	Geert Vanmeerbeeck, Ziduo Lin, Abdulkadir Yurt, Richard Stahl, and Andy Lambrechts	Interuniversitair Microelektronica Centrum vzw IMEC	Yes	Yes	Optical (light scattering and image sensor (CCD or CMOS camera))	Pollen, molds, fungi, bacteria, dust, soot, and other pollutants	External device	Fixed	Indoor and outdoor	Not mentioned	Yes	Not implemented
J	EP4085246B1 [68]	Device for detecting the presence of pollen in the air, and corresponding detection method	Active	21 December 2020	Jérôme Richard, Johann Lauthier, and Jean-Baptiste Renard	Lify Air, Centre National de la Recherche Scientifique CNRS, Universite dOrleans	Yes	Yes	Optical sensor (light scattering)	Pollen and meteorological conditions (temperature, atmospheric pressure, relative humidity, luminosity, precipitation, and wind speed)	External device	Fixed	Outdoor	Not mentioned	Yes	Machine learning
K	CA3183076A1 [70]	Method for the detection of aerosol particles in ambient air	Pending	21 July 2021	Achim Bittner, Alfons Dehe, and Rebecca Wienbruch	Hann-Schickard-Gesellschaft fuer Angewand-te Forschung eV	Yes	Yes	Photoacoustic gas sensor (MEMS sensor)	Bioaerosols, spores, bacteria, and viruses	External device	Fixed	Not specified	Uses reference data obtained from a calibration chamber with known aerosol concentrations	No; waiting time not specified	Not implemented
L	US11490852B1 [60]	Wearable device for detecting microorganisms, sterilizing pathogens, and environmental monitoring	Active	9 August 2021	Hemal B Kurani and Hetal B Kurani	Individual	Yes	Yes	Optical sensor (light scattering and image sensor)	Pollen, pathogens, microorganisms, dust mite allergens, and environmental conditions	Integrated display	Mobile	Indoor and outdoor	Linear sensors used in the device are calibrated by measuring voltage levels when in contact with clean air or air meant to be used as a reference	Yes	Machine learning
M	JP7518413B2 [62]	Pollen forecasting device, air treatment system, pollen forecasting method, and program	Active	30 September 2022	Shiki Yu, Jun Nishino, Yuta Sasai, Kei Suzumura, and Tetsu Hashimoto	Daikin Industries Ltd.	Yes	No	Camera sensor	Pollen and dust	Integrated display	Fixed	Indoor	Not mentioned	No; 20 min waiting time	Machine learning
N	WO2023110716A1 [72]	Method and apparatus for selecting, detecting, counting and identifying pollen and/or mould spores initially in suspension in atmospheric air	Pending	12 December 2022	Caroline Paulus, Olivier Blanc, Xavier Mermet, and Jean-Maxime Roux	Commissariat A L’energie Atomique Et Aux Energies Alternatives	Yes	Yes	Optical sensor (light scattering and CMOS-type image sensors)	Pollen and mold spore	External device	Fixed	Not specified	Not mentioned	Yes	Deep learning
O	US20230358661A1 [61]	Automated airborne particulate matter collection, imaging, identification, and analysis	Pending	10 April 2023	Richard Lucas, Landon Bunderson, Nathan Allan, and Kevn Lambson	Pollen Sense LLC	Yes	Yes	Camera sensor	Pollen, dust, mold spores, bacterial cells, and soot	External device	Not specified	Not specified	Not mentioned	No; waiting time not specified	Machine learning

.

4. Discussion

The aim of comprehensively reviewing the 15 patents regarding pollen monitoring was achieved. This work complements existing scientific literature reviews, such as [36,37,75], in which no patents are reviewed. The results of this work, together with previous scientific literature reviews, supply useful information for researchers and technicians in the realm of pollen monitoring who aim to develop new technology to improve the current technological state in this field.

The most relevant data extracted from the patents were whether pollen type and pollen size are identified, the type of sensors used, and the substances that the sensors are capable of detecting. Many data that we aimed to extract during the methodology stage of this work were not found in some of the reviewed documents. This is because the inventor(s) described the patent in terms of the aim of the invention, such that some of the data we sought to extract were not available in all the patents.

4.1. Light-Scattering Sensors

Light-scattering sensors are widely used to detect concentrations of several airborne particles, including pollen. The oldest patent in the realm of pollen monitoring is patent A, and it was filed in 2003. Patent A takes advantage of the fact that pollen grains polarize light in a different direction than other particles of the same size, such that pollen grains can be identified through a light-scattering sensor. However, this invention does not allow the type nor the specific size of detected pollen grains to be identified. Other patents that use a light-scattering sensor are patents C, G, I, J, L, and N.

Although the aim was to identify the types of sensors used to recognize and count pollen, it was noticed that some light sensors, such as those used in patents I and J, not only use light-scattering sensors to measure the amount of particles in the air, but also use several light receivers to detect the angle of light deviation to estimate the size of pollen grains.

4.2. Protein Binding-Based Sensor

In 2006, patent B presented a pollen sensor based on protein binding, where the electrical resistance of the circuit in the sensor changes when proteins bind to it. Due to the sensor specificity and to the fact that different types of pollen grains present different proteins, a pollen sensor that detects pollen grains with certain proteins allows the identification of the type of detected pollen grains. However, the size of pollen grains was still not a characteristic that could be determined with existing technology found in the patents.

4.3. Cameras and Image Sensors

Using a camera or image sensor in a pollen sensor allows image processing techniques to identify pollen grains, including their type and size. Patent C was the first patent to include a camera sensor in a pollen monitoring device. Patent C uses a light-scattering sensor together with a camera sensor and a pollen database for automated pollen identification. Other patents that use a camera or image sensor are patents E, F, H, I, M, N, and O.

4.4. Real-Time Monitoring

Real-time data from a pollen monitoring device allow the prevention of and a reduction in pollen exposure among vulnerable people. In this regard, not all patents allow the obtention of real-time data, since some of them consist of capturing images and processing them separately in an external device like a computer with specialized software. Patents that do not provide real-time data are patents C, D, E, K, M, and O.

4.5. Artificial Intelligence

Using artificial intelligence in pollen monitoring devices allows automatic detection of known pollen types without requiring the user to manually identify the pollen type based on the detection of a sensor(s). The only patents that use artificial intelligence are patents H, J, L, M, N, and O, among which patents H, J, K, L, M, and O use machine learning, while patent N uses deep learning.

4.6. Additional Technological Aspects

Although reviewing patents in the realm of pollen monitoring was the main objective of this work, it was found that many inventions are capable of detecting not only pollen, but also several particles, such as dust, bacteria, viruses, fungi, and soot, among others, as well as environmental and meteorological parameters (see Table 6). Patents capable of detecting additional particles and parameters are patents D, E, G, I, J, K, L, M, N, O, and P.

Regarding the location and mobility of the reviewed patents, only patent L states that the invention corresponds to a mobile device, more specifically, a wearable device that may be used as a wristband, while patents A, B, C, E, H, I, J, K, M, and N present fixed-location devices. Patents D, F, G, and O do not specify whether the invention corresponds to a mobile or fixed-location device. Only patent M is described as intended for indoor pollen monitoring, while patent J is the only patent described as intended for outdoor pollen monitoring. Patents E, H, I, and L are suitable for both indoor and outdoor pollen monitoring. The remaining patents do not specify this information. This is because the inventors describe the patent in terms of the aim of the invention; therefore, some data that we sought to extract were not available for all patents.

Additionally, most patents do not include a display by means of which data may be visualized. The only patents to present an integrated display are patents C, H, L, and M, while patents A, B, D, E, F, I, J, K, N, and O require an external device such as a computer or a smartphone to visualize data. Patent G does not specify this information.

Some features not included in the data to be extracted during the methodology section were found, such as the use of carbon nanotubes that bind to specific proteins to detect specific pollen grains in patent B. This technique, together with image processing techniques and the use of relative humidity sensors to detect specific pollen grains based on their hygroscopic characteristics, are different ways in which pollen grains are identified in the patent literature.

Additionally, another feature found in the patents that collect samples is the modification of the electrical charge of pollen grains to attract them to a collector with the opposite electric charge.

Furthermore, the only patent that is able to identify submicrometric pollen fragments and SPPs is patent E. The identification of these particles is important due to the fact that they can trigger allergic reactions in susceptible people. Therefore, other patents that are unable to identify these particles may not be accurate when providing warnings of the levels of allergens related to pollen present in the environment.

4.7. Findings and Limitations of This Study

Something worth noticing is that most patents in this review do not describe how the calibration of the presented device is performed. The only patents providing this information are patents K and L. This is an important drawback, since daily sensor calibration is critical to ensure data accuracy and usefulness.

A finding of this research is that there are no risk assessments or thresholds specifically related to pollen and its impact on people’s health, nor is there a defined metric of what is permissible or tolerable for human beings, unlike in the case of most common air pollutants. Although there is strong evidence that pollen affects people’s quality of life, the same health technology used for other pollutants has not been applied to pollen monitoring.

A limitation of this work is the use of machine translation for patents found in a language different from English or Spanish. Although translation tools have a high accuracy and are suitable for understanding the main idea of a text, they may be limited when translating specific vocabulary from a technological field, and some terms may not be correctly translated, leading to incorrect interpretation. However, susceptibility to this problem was reduced by using several translation tools.

Additionally, another limitation of this work is that only open-access patent search engines were used to conduct the searches described in the methodology, which may have led to the exclusion of patents that are only found in paid search engines and databases.

5. Conclusions

In recent years, air pollution has aggravated the negative effects that it produces in exposed people. For this reason, monitoring the concentration of atmospheric pollutants in the air, including pollen grains, SPPs, and submicrometric pollen fragments, is currently a growing trend, and more patents in the field of pollen monitoring have become available. However, currently available pollen monitoring technology presents important drawbacks that must be addressed in order to improve risk reduction of health damage and premature death in vulnerable people. The development of devices used to monitor pollen concentrations in the air has not been as common as the development of devices used to monitor other atmospheric pollutants, such as PM, O₃, and CO₂. Among the reasons for the lack of patents available in the field of pollen monitoring is that monitoring pollen in the air requires a more complex technology implementation than the most common air pollutants that are being monitored (e.g., PM, O₃, and CO₂), due to the fact that pollen exists in different types, and different types may produce different effects in exposed people.

Most patents in this field focus on identifying pollen grain type and size by analyzing an image captured by an image sensor, most patents using an image processing technique that, if not automatic, requires time for the user to manually identify the pollen type according to the data provided by the monitoring device, which does not allow real-time pollen monitoring. For this reason, future improvements to pollen monitoring devices using image sensors and image processing techniques must be focused on automating image processing to ensure that real-time pollen data may be provided.

Another aspect of pollen monitoring that requires improvement is the limited number of mobile devices that are available in the patent literature. Since measuring atmospheric pollutants using stationary devices limits spatial resolution and makes it difficult to measure personal exposure, focusing future work on fabricating portable wearable pollen monitoring devices would allow a reduction in the risk of exposure to pollen in vulnerable people, due to the spatiotemporal resolution improvement such devices provide [76].

This work represents the first step to provide information that will help the development of new technologies in the field of pollen monitoring, benefiting the population that is susceptible to pollen allergens.